基于领域本体的亚健康中医辅助诊断系统的研究及应用
|
摘要
医学专家系统是人工智能技术应用的一个重要方向,医学诊断领域是医学专家系统的一个核心领域,因为医学的关键在于诊断技术。医学诊断辅助专家系统是医学专家系统在医学诊断领域的推广,它运用专家系统的设计原理,拥有大量专家的宝贵理论以及丰富的临床经验,模拟医学专家诊断疾病的思维过程,协助医生解决复杂的医学问题,可视为医生诊断的辅助工具,甚至能够直接为普通疾病患者提供辅助诊断而不一定需要医生的参与。
医学诊断辅助专家系统的信息处理是基于知识智能推理的系统,在功能上它是在医学领域内具有专家水平解决问题能力的系统程序。它涉及到知识获取、知识表示、知识的存储、推理控制机制以及智能人机接口的研究,是集人工智能和领域知识于一体的系统,是一个前景十分广阔的应用领域。
本文采用电子病案的形式半自动地获取知识。中医专家对患者进行诊治,与此同时助手或者专家填写电子病案,知识获取程序会自动按照定义好的框架表示形式,将电子病案获得的知识存入文本,然后知识工程师对这些病案知识进行再处理,分别形成知识框架和病案框架,最后将两种知识框架分别存入知识库和病案库。同时具体阐述了基于电子病案和模糊方法的患者自述与标准症状匹配问题。
本文通过对本体及其构建的研究,针对中医亚健康领域知识,抽象了一种建立领域本体的形式化方法,并对中医诊断领域知识进行了本体形式化描述与设计。在领域本体的驱动下进行基于中医病案的知识获取,采用基于模糊推理的方法对亚健康症状中医辅助诊断知识进行推理。并在第四章4.5节系统地介绍了基于公理的中医脉诊知识分析与推理。
最后本文把以上方法应用到了中医亚健康辅助诊断领域,具体介绍了中医亚健康辅助诊断知识(规则)库、症状库、证候库的分析与设计,并实现了中医亚健康辅助诊断专家系统的证候推理过程。
作为人工智能一个重要分支的专家系统(Expert System,ES)是在20世纪60年代初期产生并发展起来的一门新兴的应用科学,而且正随着计算机技术的不断发展而日臻完善和成熟。专家系统是一种智能的计算机程序。这种程序使用知识与推理过程,求解那些需要杰出人物的专门知识才能求解的复杂问题,它能像专家一样解决困难的和复杂的实际问题的计算机(软件)系统。
知识表示是为描述世界所作的一组约定,是知识的符号化、形式化或模型化,各种不同的知识表示方法,是各种不同的形式化的知识模型。知识表示的研究既要考虑知识的表示与存储,又要考虑知识的使用。
用自然语言与计算机进行通信,这是人类长期以来所追求的。自然语言的识别和处理是人工智能研究的最重要的课题之一,也是人工智能研究的关键。如何去获取各种不同的知识,并以一种计算机可以使用和处理的方法表达知识是知识获取的根本问题。拥用知识是专家系统有别于其它计算机软件系统的重要标志,而知识的质量与数量又是决定专家系统性能的关键因素,但如何使专家系统获得高质量的知识,正是知识获取要解决的问题。知识获取的基本任务是为专家系统获取知识,建立起健全、完善、有效的知识库,以满足求解领域问题的需要。
案例学习(CBI)是人工智能中的一种学习方法,该方法由一系列大同小异的学习策略组成,依靠过去的经验进行学习和求解问题.新的案例可以通过修改案例库中与当前情况相似的旧案例来获得。基于案例的推理技术尝试将叙述能力、知识整理进行融合,对有关问题的事件或案例的知识进行萃取。
本体是概念化的明确的规范说明。本体可以表示不同的事物：术语表和数据词典,叙词表和分类法,框架和数据模型,形式本体和参考等。一个本体其实就是一个用某种本体语言表达的控制词表,该语言以语法规则限定了词表术语表达具体领域内容的方法,该语法形式上规定了本体控制词表的术语如何共同使用。以详细程度和领域依赖度两个维度作为对本体划分的基础,详细程度高的称作参考本体,详细程度低的称为共享本体。依照领域依赖程度,可以细分为顶级、领域、任务和应用本体等4类。另外,根据主题可分为知识表示本体、通用本体、领域本体、术语本体和任务本体；根据形式化程度分为完全非形式化、结构非形式化.半形式化、形式化的本体。
在科学研究和日常生活中,人们一直在追求用一确定的数学模型或康托集合概念来解决问题或表征现象。但专家系统的问题求解一般不象数学、物理等学科那样具有严密性和精确性,它处理的信息往往是不确定的、不精确性的、不完全知道的,甚至是模糊的、不完备的。造成这种现象的原因主要有两点：一是推理依据的规则(或知识)不精确、不完善,而且对不同流派来说还是不一致的；二是证据本身的不确定、不完全甚至有干扰。因此,专家系统设计中不精确的推理使用,几乎是难于避免的,有时成为一个涉及到专家系统设计成败的重要问题。其中有代表性的是如下四种方法：确定性理论、主观Bayes方法、证据理论、模糊集理论。不管是哪一种不精确推理模型,尽管它们处理问题的基本思想和方法有很大差异,但本质是相同的,即都有相同的结构形式,即如下三部分：1)知识不确定性的描述；2)证据不确定性的描述；3)不确定性的更新算法。
不精确推理的核心思想是在基于规则的专家系统中,为每个公理本身赋予一个不确定性度量,再给出一组算法,在此基础上,就可以通过这组算法,由公理的不确定性求出定理的不确定性。
模糊集理论是一种处理模糊现象的一个极好方法。它多应用于预测型的专家系统中,如经济预测、气象预报、战略布署等。它引起不确定性原因是由模糊性所引起的。它采用隶属函数这种效值计算方法来表达不确定性。其核心思想是要确定诸如：可能性、可能性分布、可能性分布函数、条件可能性分布函数,.边缘可能性分布函数等几个度量和它们之问的关系,以及各种模糊命题的转换规则和不精确命题的推理规则等等。
Medical Expert System is an important direction of the application of artificial intelligence technology. The field of medical diagnostic is a core area of Medical Expert System, because medical diagnostic technology is the key of medical field. Medical diagnosis assistant expert system is a promotion of medical diagnosis field,and it uses the design principle of expert system and holds a great deal of valuable expert experience and a wealth of clinical experience.This system can simulate the thinking process of experts diagnosed the disease,and assist to solve complex Medical problems for doctors. It can be seen as an assistant tool of doctor diagnosis,and even could direct provide assistant diagnosis to common patients without doctors paticipating.
The information processing of Medical associate experts system is based on the system of knowledge intelligent reasoning. The functions of Medical associate experts system have resolving problem capability in medical field.This system comes down to researchs of knowledge acquisition, knowledge representation, knowledge storage reasoning control and human-computer interface,and it is a system of unity artificial intelligence and domain knowledge,and it is a promising prospect in the application areas.
In this paper using the form of semi-automatic to acquisition the electronic medical case. Chinese experts diagnosis and treatment for patients, at the same time assistants or experts fill out the electronic medical case. Knowledge acquisition program could automatically storage these information from electronic medical case according with the form framework into text.After that knowledge engineers dispose them in detail, and respectively creat knowledge framework and cases framework.At last,let those two kind of knowledge storage into knowledge base and case base.At the same time, and expound the matching program of patient's description and standard symptom based on electronic medical case and fuzzy method.
Through the research of ontology and ontology's constructed,this paper abstract establishment a formal method of domian ontology aims at the domain knowledge of TCM(traditional chinese medicine) sub-health,and makes a formal describing and designing on ontology to TCM diagnosis domain knowledge.Then implements knowledge acquisition on TCM cases by the method of ontology,and bases on fuzzy reasoning method to consequence the knowledge.In section 4.5,make an analysis and reasoning of pulse diagnosis based on TCM axiom method.
At last using above methods to the field of TCM sub-health associate diagnosis,introduce the analysis and design of knowledge(rules)base,symptom base and syndrome base of TCM sub-health associate diagnosis system and realize the syndrome reasoning process of a TCM sub-health associate diagnosis expert system.
As an important branch of artificial intelligence expert system (Expert System, ES) in the early 1960s and have developed the application of an emerging science, and we are with the continuous development of computer technology and improving and maturing. Expert System is an intelligent computer programs. Such a procedure using knowledge and reasoning process, for those who need the expertise of outstanding personalities in order to solve the complex problems, such as expert as it can solve its problems and the practical problems of complex computer (software) system.
Knowledge that the world is described by a group of agreement, is the symbol of knowledge, formal or model, a variety of ways that knowledge is a variety of formal knowledge model. Knowledge that the study should not only consider the knowledge that with the storage, but also consider the use of knowledge.
Using natural language and computer communications, this is a long time pursued by mankind. The natural language recognition and artificial intelligence research is handling the most important issue is one of the key study of artificial intelligence. How to access a variety of knowledge and to a computer can use the methods and process of knowledge acquisition of knowledge is a fundamental issue. Yong is the expert system with the knowledge of computer software system is different from other important indicators of, and knowledge of the quality and quantity is the decision of experts the key factor in system performance, but how the experts access to high-quality system of knowledge, knowledge acquisition is to solve Problems. The basic task of knowledge acquisition expert system for access to knowledge and establish a sound, sound, effective knowledge base to meet the needs of solving the problem areas.
Case studies (CBI) is the artificial intelligence of a learning method, the method of learning from a series of more or less the same strategy of relying on past experience, learning and solving problems. New case may amend the case with the current situation similar to the old case to get. Case-based reasoning technology will try to describe the ability and knowledge to organize the integration of the issues involved incidents or cases of knowledge extraction.
Ontology is the conceptualization of a clear specification. The body can express different things:a glossary and data from the Syrian vocabulary and classification, framework and data model, and other forms of body and reference. In fact, a body is a body language with a word of the control table, the language grammar rules to limit the vocabulary to express specific areas in terms of content means that the grammatical form the bulk requirements Vocabulary control how the terminology used. To the level of detail and dependence on the field as the two dimensions of the bulk of the foundation, the higher the level of detail as reference ontology, the low level of detail as sharing body. In accordance with the degree of dependence on the field, can be broken down into the top, the field, tasks and applications such as body four categories. In addition, under the theme of knowledge that can be divided into the body, common body, the area of the body, body and the task of ontology terms, according to the degree into completely non-formal Formal, non-formal structure. Semi-Formal, the formal body.
In scientific research and daily life, people have always been used in the pursuit of a set of mathematical models or Cantor collection concept to solve the problem or the characterization of the phenomenon. However, expert system to solve the problems generally do not like mathematics, physics and other disciplines as a tight and accuracy, processing of information is often uncertain, not accuracy, not fully aware, even vague, incomplete. The cause of this phenomenon are two main reasons: First, reasoning based on the rules (or knowledge) imprecise, incomplete, but also to different schools, or inconsistency and the other is in itself evidence of uncertainty, not entirely or even interference. Therefore, experts in system design using imprecise reasoning, is almost difficult to avoid, and sometimes become involved in an expert system to design the success or failure of important issues. Which is representative of the following four methods:the uncertainty theory, subjective Bayes methods, the theory of evidence, fuzzy set theory. No matter what kind of imprecise reasoning model, even though they deal with the basic ideas and methods are very different, but the essence is the same, that is, have the same structure, namely the following three parts:1) a description of the uncertainty of knowledge 2) a description of the uncertainty of evidence,3) the uncertainty of the updated algorithm.
Imprecise reasoning is the core idea in the rule-based expert system, for each of Justice itself gives a measure uncertainty, and then presented a set of algorithms, on the basis of this, we can pass this group algorithm, not by justice Uncertainty obtained theorem of uncertainty.
Fuzzy set theory is a fuzzy deal with the phenomenon of an excellent method. It used more than the forecast of expert systems, such as economic forecasts, weather forecasts, strategic deployment, and so on. It is caused by the uncertainty caused by the ambiguous. It uses this function under the validity of calculation methods to express uncertainty. Its core idea is to identify such as:the possibility of possibility, the possibility distribution functions, conditions for the possibility distribution function, the edge of possibility distribution function, and several of the measure and the relationship between them, and various fuzzy proposition conversion rules Proposition reasoning and imprecise rules, and so on.
Medical Expert System is an important direction of the application of artificial intelligence technology. The field of medical diagnostic is a core area of Medical Expert System, because medical diagnostic technology is the key of medical field. Medical diagnosis assistant expert system is a promotion of medical diagnosis field,and it uses the design principle of expert system and holds a great deal of valuable expert experience and a wealth of clinical experience.This system can simulate the thinking process of experts diagnosed the disease,and assist to solve complex Medical problems for doctors. It can be seen as an assistant tool of doctor diagnosis,and even could direct provide assistant diagnosis to common patients without doctors paticipating.
The information processing of Medical associate experts system is based on the system of knowledge intelligent reasoning. The functions of Medical associate experts system have resolving problem capability in medical field.This system comes down to researchs of knowledge acquisition, knowledge representation, knowledge storage reasoning control and human-computer interface,and it is a system of unity artificial intelligence and domain knowledge,and it is a promising prospect in the application areas.
In this paper using the form of semi-automatic to acquisition the electronic medical case. Chinese experts diagnosis and treatment for patients, at the same time assistants or experts fill out the electronic medical case. Knowledge acquisition program could automatically storage these information from electronic medical case according with the form framework into text.After that knowledge engineers dispose them in detail, and respectively creat knowledge framework and cases framework.At last,let those two kind of knowledge storage into knowledge base and case base.At the same time, and expound the matching program of patient's description and standard symptom based on electronic medical case and fuzzy method.
Through the research of ontology and ontology's constructed,this paper abstract establishment a formal method of domian ontology aims at the domain knowledge of TCM(traditional chinese medicine) sub-health,and makes a formal describing and designing on ontology to TCM diagnosis domain knowledge.Then implements knowledge acquisition on TCM cases by the method of ontology,and bases on fuzzy reasoning method to consequence the knowledge.In section 4.5,make an analysis and reasoning of pulse diagnosis based on TCM axiom method.
At last using above methods to the field of TCM sub-health associate diagnosis,introduce the analysis and design of knowledge(rules)base,symptom base and syndrome base of TCM sub-health associate diagnosis system and realize the syndrome reasoning process of a TCM sub-health associate diagnosis expert system.
As an important branch of artificial intelligence expert system (Expert System, ES) in the early 1960s and have developed the application of an emerging science, and we are with the continuous development of computer technology and improving and maturing. Expert System is an intelligent computer programs. Such a procedure using knowledge and reasoning process, for those who need the expertise of outstanding personalities in order to solve the complex problems, such as expert as it can solve its problems and the practical problems of complex computer (software) system.
Knowledge that the world is described by a group of agreement, is the symbol of knowledge, formal or model, a variety of ways that knowledge is a variety of formal knowledge model. Knowledge that the study should not only consider the knowledge that with the storage, but also consider the use of knowledge.
Using natural language and computer communications, this is a long time pursued by mankind. The natural language recognition and artificial intelligence research is handling the most important issue is one of the key study of artificial intelligence. How to access a variety of knowledge and to a computer can use the methods and process of knowledge acquisition of knowledge is a fundamental issue. Yong is the expert system with the knowledge of computer software system is different from other important indicators of, and knowledge of the quality and quantity is the decision of experts the key factor in system performance, but how the experts access to high-quality system of knowledge, knowledge acquisition is to solve Problems. The basic task of knowledge acquisition expert system for access to knowledge and establish a sound, sound, effective knowledge base to meet the needs of solving the problem areas.
Case studies (CBI) is the artificial intelligence of a learning method, the method of learning from a series of more or less the same strategy of relying on past experience, learning and solving problems. New case may amend the case with the current situation similar to the old case to get. Case-based reasoning technology will try to describe the ability and knowledge to organize the integration of the issues involved incidents or cases of knowledge extraction.
Ontology is the conceptualization of a clear specification. The body can express different things:a glossary and data from the Syrian vocabulary and classification, framework and data model, and other forms of body and reference. In fact, a body is a body language with a word of the control table, the language grammar rules to limit the vocabulary to express specific areas in terms of content means that the grammatical form the bulk requirements Vocabulary control how the terminology used. To the level of detail and dependence on the field as the two dimensions of the bulk of the foundation, the higher the level of detail as reference ontology, the low level of detail as sharing body. In accordance with the degree of dependence on the field, can be broken down into the top, the field, tasks and applications such as body four categories. In addition, under the theme of knowledge that can be divided into the body, common body, the area of the body, body and the task of ontology terms, according to the degree into completely non-formal Formal, non-formal structure. Semi-Formal, the formal body.
In scientific research and daily life, people have always been used in the pursuit of a set of mathematical models or Cantor collection concept to solve the problem or the characterization of the phenomenon. However, expert system to solve the problems generally do not like mathematics, physics and other disciplines as a tight and accuracy, processing of information is often uncertain, not accuracy, not fully aware, even vague, incomplete. The cause of this phenomenon are two main reasons:First, reasoning based on the rules (or knowledge) imprecise, incomplete, but also to different schools, or inconsistency and the other is in itself evidence of uncertainty, not entirely or even interference. Therefore, experts in system design using imprecise reasoning, is almost difficult to avoid, and sometimes become involved in an expert system to design the success or failure of important issues. Which is representative of the following four methods:the uncertainty theory, subjective Bayes methods, the theory of evidence, fuzzy set theory. No matter what kind of imprecise reasoning model, even though they deal with the basic ideas and methods are very different, but the essence is the same, that is, have the same structure, namely the following three parts:1) a description of the uncertainty of knowledge 2) a description of the uncertainty of evidence,3) the uncertainty of the updated algorithm.
Imprecise reasoning is the core idea in the rule-based expert system, for each of Justice itself gives a measure uncertainty, and then presented a set of algorithms, on the basis of this, we can pass this group algorithm, not by justice Uncertainty obtained theorem of uncertainty.
Fuzzy set theory is a fuzzy deal with the phenomenon of an excellent method. It used more than the forecast of expert systems, such as economic forecasts, weather forecasts, strategic deployment, and so on. It is caused by the uncertainty caused by the ambiguous. It uses this function under the validity of calculation methods to express uncertainty. Its core idea is to identify such as:the possibility of possibility, the possibility distribution functions, conditions for the possibility distribution function, the edge of possibility distribution function, and several of the measure and the relationship between them, and various fuzzy proposition conversion rules Proposition reasoning and imprecise rules, and so on.
医学诊断辅助专家系统的信息处理是基于知识智能推理的系统,在功能上它是在医学领域内具有专家水平解决问题能力的系统程序。它涉及到知识获取、知识表示、知识的存储、推理控制机制以及智能人机接口的研究,是集人工智能和领域知识于一体的系统,是一个前景十分广阔的应用领域。
本文采用电子病案的形式半自动地获取知识。中医专家对患者进行诊治,与此同时助手或者专家填写电子病案,知识获取程序会自动按照定义好的框架表示形式,将电子病案获得的知识存入文本,然后知识工程师对这些病案知识进行再处理,分别形成知识框架和病案框架,最后将两种知识框架分别存入知识库和病案库。同时具体阐述了基于电子病案和模糊方法的患者自述与标准症状匹配问题。
本文通过对本体及其构建的研究,针对中医亚健康领域知识,抽象了一种建立领域本体的形式化方法,并对中医诊断领域知识进行了本体形式化描述与设计。在领域本体的驱动下进行基于中医病案的知识获取,采用基于模糊推理的方法对亚健康症状中医辅助诊断知识进行推理。并在第四章4.5节系统地介绍了基于公理的中医脉诊知识分析与推理。
最后本文把以上方法应用到了中医亚健康辅助诊断领域,具体介绍了中医亚健康辅助诊断知识(规则)库、症状库、证候库的分析与设计,并实现了中医亚健康辅助诊断专家系统的证候推理过程。
作为人工智能一个重要分支的专家系统(Expert System,ES)是在20世纪60年代初期产生并发展起来的一门新兴的应用科学,而且正随着计算机技术的不断发展而日臻完善和成熟。专家系统是一种智能的计算机程序。这种程序使用知识与推理过程,求解那些需要杰出人物的专门知识才能求解的复杂问题,它能像专家一样解决困难的和复杂的实际问题的计算机(软件)系统。
知识表示是为描述世界所作的一组约定,是知识的符号化、形式化或模型化,各种不同的知识表示方法,是各种不同的形式化的知识模型。知识表示的研究既要考虑知识的表示与存储,又要考虑知识的使用。
用自然语言与计算机进行通信,这是人类长期以来所追求的。自然语言的识别和处理是人工智能研究的最重要的课题之一,也是人工智能研究的关键。如何去获取各种不同的知识,并以一种计算机可以使用和处理的方法表达知识是知识获取的根本问题。拥用知识是专家系统有别于其它计算机软件系统的重要标志,而知识的质量与数量又是决定专家系统性能的关键因素,但如何使专家系统获得高质量的知识,正是知识获取要解决的问题。知识获取的基本任务是为专家系统获取知识,建立起健全、完善、有效的知识库,以满足求解领域问题的需要。
案例学习(CBI)是人工智能中的一种学习方法,该方法由一系列大同小异的学习策略组成,依靠过去的经验进行学习和求解问题.新的案例可以通过修改案例库中与当前情况相似的旧案例来获得。基于案例的推理技术尝试将叙述能力、知识整理进行融合,对有关问题的事件或案例的知识进行萃取。
本体是概念化的明确的规范说明。本体可以表示不同的事物：术语表和数据词典,叙词表和分类法,框架和数据模型,形式本体和参考等。一个本体其实就是一个用某种本体语言表达的控制词表,该语言以语法规则限定了词表术语表达具体领域内容的方法,该语法形式上规定了本体控制词表的术语如何共同使用。以详细程度和领域依赖度两个维度作为对本体划分的基础,详细程度高的称作参考本体,详细程度低的称为共享本体。依照领域依赖程度,可以细分为顶级、领域、任务和应用本体等4类。另外,根据主题可分为知识表示本体、通用本体、领域本体、术语本体和任务本体；根据形式化程度分为完全非形式化、结构非形式化.半形式化、形式化的本体。
在科学研究和日常生活中,人们一直在追求用一确定的数学模型或康托集合概念来解决问题或表征现象。但专家系统的问题求解一般不象数学、物理等学科那样具有严密性和精确性,它处理的信息往往是不确定的、不精确性的、不完全知道的,甚至是模糊的、不完备的。造成这种现象的原因主要有两点：一是推理依据的规则(或知识)不精确、不完善,而且对不同流派来说还是不一致的；二是证据本身的不确定、不完全甚至有干扰。因此,专家系统设计中不精确的推理使用,几乎是难于避免的,有时成为一个涉及到专家系统设计成败的重要问题。其中有代表性的是如下四种方法：确定性理论、主观Bayes方法、证据理论、模糊集理论。不管是哪一种不精确推理模型,尽管它们处理问题的基本思想和方法有很大差异,但本质是相同的,即都有相同的结构形式,即如下三部分：1)知识不确定性的描述；2)证据不确定性的描述；3)不确定性的更新算法。
不精确推理的核心思想是在基于规则的专家系统中,为每个公理本身赋予一个不确定性度量,再给出一组算法,在此基础上,就可以通过这组算法,由公理的不确定性求出定理的不确定性。
模糊集理论是一种处理模糊现象的一个极好方法。它多应用于预测型的专家系统中,如经济预测、气象预报、战略布署等。它引起不确定性原因是由模糊性所引起的。它采用隶属函数这种效值计算方法来表达不确定性。其核心思想是要确定诸如：可能性、可能性分布、可能性分布函数、条件可能性分布函数,.边缘可能性分布函数等几个度量和它们之问的关系,以及各种模糊命题的转换规则和不精确命题的推理规则等等。
Medical Expert System is an important direction of the application of artificial intelligence technology. The field of medical diagnostic is a core area of Medical Expert System, because medical diagnostic technology is the key of medical field. Medical diagnosis assistant expert system is a promotion of medical diagnosis field,and it uses the design principle of expert system and holds a great deal of valuable expert experience and a wealth of clinical experience.This system can simulate the thinking process of experts diagnosed the disease,and assist to solve complex Medical problems for doctors. It can be seen as an assistant tool of doctor diagnosis,and even could direct provide assistant diagnosis to common patients without doctors paticipating.
The information processing of Medical associate experts system is based on the system of knowledge intelligent reasoning. The functions of Medical associate experts system have resolving problem capability in medical field.This system comes down to researchs of knowledge acquisition, knowledge representation, knowledge storage reasoning control and human-computer interface,and it is a system of unity artificial intelligence and domain knowledge,and it is a promising prospect in the application areas.
In this paper using the form of semi-automatic to acquisition the electronic medical case. Chinese experts diagnosis and treatment for patients, at the same time assistants or experts fill out the electronic medical case. Knowledge acquisition program could automatically storage these information from electronic medical case according with the form framework into text.After that knowledge engineers dispose them in detail, and respectively creat knowledge framework and cases framework.At last,let those two kind of knowledge storage into knowledge base and case base.At the same time, and expound the matching program of patient's description and standard symptom based on electronic medical case and fuzzy method.
Through the research of ontology and ontology's constructed,this paper abstract establishment a formal method of domian ontology aims at the domain knowledge of TCM(traditional chinese medicine) sub-health,and makes a formal describing and designing on ontology to TCM diagnosis domain knowledge.Then implements knowledge acquisition on TCM cases by the method of ontology,and bases on fuzzy reasoning method to consequence the knowledge.In section 4.5,make an analysis and reasoning of pulse diagnosis based on TCM axiom method.
At last using above methods to the field of TCM sub-health associate diagnosis,introduce the analysis and design of knowledge(rules)base,symptom base and syndrome base of TCM sub-health associate diagnosis system and realize the syndrome reasoning process of a TCM sub-health associate diagnosis expert system.
As an important branch of artificial intelligence expert system (Expert System, ES) in the early 1960s and have developed the application of an emerging science, and we are with the continuous development of computer technology and improving and maturing. Expert System is an intelligent computer programs. Such a procedure using knowledge and reasoning process, for those who need the expertise of outstanding personalities in order to solve the complex problems, such as expert as it can solve its problems and the practical problems of complex computer (software) system.
Knowledge that the world is described by a group of agreement, is the symbol of knowledge, formal or model, a variety of ways that knowledge is a variety of formal knowledge model. Knowledge that the study should not only consider the knowledge that with the storage, but also consider the use of knowledge.
Using natural language and computer communications, this is a long time pursued by mankind. The natural language recognition and artificial intelligence research is handling the most important issue is one of the key study of artificial intelligence. How to access a variety of knowledge and to a computer can use the methods and process of knowledge acquisition of knowledge is a fundamental issue. Yong is the expert system with the knowledge of computer software system is different from other important indicators of, and knowledge of the quality and quantity is the decision of experts the key factor in system performance, but how the experts access to high-quality system of knowledge, knowledge acquisition is to solve Problems. The basic task of knowledge acquisition expert system for access to knowledge and establish a sound, sound, effective knowledge base to meet the needs of solving the problem areas.
Case studies (CBI) is the artificial intelligence of a learning method, the method of learning from a series of more or less the same strategy of relying on past experience, learning and solving problems. New case may amend the case with the current situation similar to the old case to get. Case-based reasoning technology will try to describe the ability and knowledge to organize the integration of the issues involved incidents or cases of knowledge extraction.
Ontology is the conceptualization of a clear specification. The body can express different things:a glossary and data from the Syrian vocabulary and classification, framework and data model, and other forms of body and reference. In fact, a body is a body language with a word of the control table, the language grammar rules to limit the vocabulary to express specific areas in terms of content means that the grammatical form the bulk requirements Vocabulary control how the terminology used. To the level of detail and dependence on the field as the two dimensions of the bulk of the foundation, the higher the level of detail as reference ontology, the low level of detail as sharing body. In accordance with the degree of dependence on the field, can be broken down into the top, the field, tasks and applications such as body four categories. In addition, under the theme of knowledge that can be divided into the body, common body, the area of the body, body and the task of ontology terms, according to the degree into completely non-formal Formal, non-formal structure. Semi-Formal, the formal body.
In scientific research and daily life, people have always been used in the pursuit of a set of mathematical models or Cantor collection concept to solve the problem or the characterization of the phenomenon. However, expert system to solve the problems generally do not like mathematics, physics and other disciplines as a tight and accuracy, processing of information is often uncertain, not accuracy, not fully aware, even vague, incomplete. The cause of this phenomenon are two main reasons: First, reasoning based on the rules (or knowledge) imprecise, incomplete, but also to different schools, or inconsistency and the other is in itself evidence of uncertainty, not entirely or even interference. Therefore, experts in system design using imprecise reasoning, is almost difficult to avoid, and sometimes become involved in an expert system to design the success or failure of important issues. Which is representative of the following four methods:the uncertainty theory, subjective Bayes methods, the theory of evidence, fuzzy set theory. No matter what kind of imprecise reasoning model, even though they deal with the basic ideas and methods are very different, but the essence is the same, that is, have the same structure, namely the following three parts:1) a description of the uncertainty of knowledge 2) a description of the uncertainty of evidence,3) the uncertainty of the updated algorithm.
Imprecise reasoning is the core idea in the rule-based expert system, for each of Justice itself gives a measure uncertainty, and then presented a set of algorithms, on the basis of this, we can pass this group algorithm, not by justice Uncertainty obtained theorem of uncertainty.
Fuzzy set theory is a fuzzy deal with the phenomenon of an excellent method. It used more than the forecast of expert systems, such as economic forecasts, weather forecasts, strategic deployment, and so on. It is caused by the uncertainty caused by the ambiguous. It uses this function under the validity of calculation methods to express uncertainty. Its core idea is to identify such as:the possibility of possibility, the possibility distribution functions, conditions for the possibility distribution function, the edge of possibility distribution function, and several of the measure and the relationship between them, and various fuzzy proposition conversion rules Proposition reasoning and imprecise rules, and so on.
Medical Expert System is an important direction of the application of artificial intelligence technology. The field of medical diagnostic is a core area of Medical Expert System, because medical diagnostic technology is the key of medical field. Medical diagnosis assistant expert system is a promotion of medical diagnosis field,and it uses the design principle of expert system and holds a great deal of valuable expert experience and a wealth of clinical experience.This system can simulate the thinking process of experts diagnosed the disease,and assist to solve complex Medical problems for doctors. It can be seen as an assistant tool of doctor diagnosis,and even could direct provide assistant diagnosis to common patients without doctors paticipating.
The information processing of Medical associate experts system is based on the system of knowledge intelligent reasoning. The functions of Medical associate experts system have resolving problem capability in medical field.This system comes down to researchs of knowledge acquisition, knowledge representation, knowledge storage reasoning control and human-computer interface,and it is a system of unity artificial intelligence and domain knowledge,and it is a promising prospect in the application areas.
In this paper using the form of semi-automatic to acquisition the electronic medical case. Chinese experts diagnosis and treatment for patients, at the same time assistants or experts fill out the electronic medical case. Knowledge acquisition program could automatically storage these information from electronic medical case according with the form framework into text.After that knowledge engineers dispose them in detail, and respectively creat knowledge framework and cases framework.At last,let those two kind of knowledge storage into knowledge base and case base.At the same time, and expound the matching program of patient's description and standard symptom based on electronic medical case and fuzzy method.
Through the research of ontology and ontology's constructed,this paper abstract establishment a formal method of domian ontology aims at the domain knowledge of TCM(traditional chinese medicine) sub-health,and makes a formal describing and designing on ontology to TCM diagnosis domain knowledge.Then implements knowledge acquisition on TCM cases by the method of ontology,and bases on fuzzy reasoning method to consequence the knowledge.In section 4.5,make an analysis and reasoning of pulse diagnosis based on TCM axiom method.
At last using above methods to the field of TCM sub-health associate diagnosis,introduce the analysis and design of knowledge(rules)base,symptom base and syndrome base of TCM sub-health associate diagnosis system and realize the syndrome reasoning process of a TCM sub-health associate diagnosis expert system.
As an important branch of artificial intelligence expert system (Expert System, ES) in the early 1960s and have developed the application of an emerging science, and we are with the continuous development of computer technology and improving and maturing. Expert System is an intelligent computer programs. Such a procedure using knowledge and reasoning process, for those who need the expertise of outstanding personalities in order to solve the complex problems, such as expert as it can solve its problems and the practical problems of complex computer (software) system.
Knowledge that the world is described by a group of agreement, is the symbol of knowledge, formal or model, a variety of ways that knowledge is a variety of formal knowledge model. Knowledge that the study should not only consider the knowledge that with the storage, but also consider the use of knowledge.
Using natural language and computer communications, this is a long time pursued by mankind. The natural language recognition and artificial intelligence research is handling the most important issue is one of the key study of artificial intelligence. How to access a variety of knowledge and to a computer can use the methods and process of knowledge acquisition of knowledge is a fundamental issue. Yong is the expert system with the knowledge of computer software system is different from other important indicators of, and knowledge of the quality and quantity is the decision of experts the key factor in system performance, but how the experts access to high-quality system of knowledge, knowledge acquisition is to solve Problems. The basic task of knowledge acquisition expert system for access to knowledge and establish a sound, sound, effective knowledge base to meet the needs of solving the problem areas.
Case studies (CBI) is the artificial intelligence of a learning method, the method of learning from a series of more or less the same strategy of relying on past experience, learning and solving problems. New case may amend the case with the current situation similar to the old case to get. Case-based reasoning technology will try to describe the ability and knowledge to organize the integration of the issues involved incidents or cases of knowledge extraction.
Ontology is the conceptualization of a clear specification. The body can express different things:a glossary and data from the Syrian vocabulary and classification, framework and data model, and other forms of body and reference. In fact, a body is a body language with a word of the control table, the language grammar rules to limit the vocabulary to express specific areas in terms of content means that the grammatical form the bulk requirements Vocabulary control how the terminology used. To the level of detail and dependence on the field as the two dimensions of the bulk of the foundation, the higher the level of detail as reference ontology, the low level of detail as sharing body. In accordance with the degree of dependence on the field, can be broken down into the top, the field, tasks and applications such as body four categories. In addition, under the theme of knowledge that can be divided into the body, common body, the area of the body, body and the task of ontology terms, according to the degree into completely non-formal Formal, non-formal structure. Semi-Formal, the formal body.
In scientific research and daily life, people have always been used in the pursuit of a set of mathematical models or Cantor collection concept to solve the problem or the characterization of the phenomenon. However, expert system to solve the problems generally do not like mathematics, physics and other disciplines as a tight and accuracy, processing of information is often uncertain, not accuracy, not fully aware, even vague, incomplete. The cause of this phenomenon are two main reasons:First, reasoning based on the rules (or knowledge) imprecise, incomplete, but also to different schools, or inconsistency and the other is in itself evidence of uncertainty, not entirely or even interference. Therefore, experts in system design using imprecise reasoning, is almost difficult to avoid, and sometimes become involved in an expert system to design the success or failure of important issues. Which is representative of the following four methods:the uncertainty theory, subjective Bayes methods, the theory of evidence, fuzzy set theory. No matter what kind of imprecise reasoning model, even though they deal with the basic ideas and methods are very different, but the essence is the same, that is, have the same structure, namely the following three parts:1) a description of the uncertainty of knowledge 2) a description of the uncertainty of evidence,3) the uncertainty of the updated algorithm.
Imprecise reasoning is the core idea in the rule-based expert system, for each of Justice itself gives a measure uncertainty, and then presented a set of algorithms, on the basis of this, we can pass this group algorithm, not by justice Uncertainty obtained theorem of uncertainty.
Fuzzy set theory is a fuzzy deal with the phenomenon of an excellent method. It used more than the forecast of expert systems, such as economic forecasts, weather forecasts, strategic deployment, and so on. It is caused by the uncertainty caused by the ambiguous. It uses this function under the validity of calculation methods to express uncertainty. Its core idea is to identify such as:the possibility of possibility, the possibility distribution functions, conditions for the possibility distribution function, the edge of possibility distribution function, and several of the measure and the relationship between them, and various fuzzy proposition conversion rules Proposition reasoning and imprecise rules, and so on.
引文
[1]Borst W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis, University of Twente,Enschede,1997 [J]
[3]Kai Mertins, Peter Heisig, Jens Vorbeck et. al. Knowledge Management Concepts and Best Practices. Springer-Verleg Berlin Heidelbeg New York,2003 [J]
[2]Studer R, Benjamins V R, Fensel D. Knowledge Engineering, Principles and Methods. Data and Knowledge Engineering,1998,25(122):161-197 [J]
[4]Neches R, Fikes R E, Gruber T R, etal. Enabling Technology for Knowledge Sharing. AIMagazine,1991,12(3):36～56 [J]
[5]Daniel Jurafsky & James H.Martin. Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Publishing House of Electronics Industry,2005 [J]
[6]Gruber T R. A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition,1993,5:199-220 [J]
[7]Guarino N. Semantic Matching:Formal Ontological Distinctions for Information Organization, Extraction,and Integration. In:Pazienza M T, eds. Information Extraction:A Multidisciplinary Approach to an Emerging Information Technology,Springer Verlag,1997,139～170 [J]
[8]Uschold M. Building Ontologies:Towards A Unified Methodology. In expert systems 96,1996Wordnet. http://www. cogsci. princeton. Edu, Framenet. http://www. icsi. berkeley. edu
[9]Gruber T R. Towards Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal of Hu2man2Computer Studies,1995,43:907-928 [J]
[10]Guarino N,Welty C. A Formal Ontology of Properties. In:Dieg R, Corby 0, eds. the Proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2000), Springer Verlag,2000,97～112 [J]
[11]Guarino N,Masolo C,Vetere G. OntoSeek:Content2Based Access to the Web. IEEE Intelligent Systems,1999,14(3):70～80 [J]
[12]Perez A G,Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods. Workshop on Ontologies and Problem2Solving Methods:Lessons Learned and Future Trends (IJCAI99), deAgosto, Estocolmo,1999 [Perez,1999] Perez A G,Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods. In:Stockholm V R,Benjamins B, Chandrasekaran A,eds. Proceedings of the IJCAI299 workshop on Ontologies and Problem2Solving Methods (KRR5) 1999,1～15 [J]
[13]Shun S B, Motta E, Domingue J. ScholOnto:an Ontology2based Digital Library Server for Research Documents and Discourse. Intl J Digital Libraries,2000,3 (3):237～ 248
[14]Bhatl CD. Mangement strategies for individual knowledge and organizational knowledge[J]. JKnowledgeManagement,2002,6(1):31-39
[15]Chandra S. Amaravadi, The Dimensions of Process Knowledge,Knowledge and Process Management,2005;12(1)
[16]Injun Choi, Jisoo Jung, Minseok Song. A Framework for the Integration of Knowledge Management and Business Process Management. Int. J. Innovationand Leaning,2004; (4 [J])
[17]付相君,李善平,郭鸣,产品数据模型的本体知识表达,计算机辅助设计与图形学学报,Mar,2005, vol.17 NO.3 [J]
[18]裴道武,关于模糊逻辑与模糊推理逻辑基础问题的十年研究综述,工程数学学报,[J]
[19]顾金睿,王芳关于本体论的研究综述情报科学June,2007, Vol,25, No.6 [J]
[20]夏锦文基于XML的本体知识建模语言九江师专学报(自然科学版)June,2003, No.6[I]
[21]傅泽田,郭永洪,敖丽敏,基于本体知识的诊断推理集成模型农业系统科学与综合研
究,May,2004,Vol.20,No.2[J]
[22]赵波,夏幼明,王泳,解继丽,基于对象的本体知识库设计云南师范大学学报,Apr.2004Vol.2 No.2[J]
[23]刘晓慧,佟伟光,林树宽,基于模糊推理的专家系统的研究与实现,Apr.2007Vol.3 NO.2 [J]
[24]柯旭贵,张佑生,基于实例推理的冲压模结构设计的框架知识表示,计算机工程与应用,2002.13[J]
[25]谈理,刘谨,梅丽婷,连续生产线设备故障诊断专家系统的动态模糊推理机制的研究,2005年6月Vol.7 N0.6[J]
[26]袁磊,面向领域知识的本体知识模型XML表示框架,计算机工程,January 2006
[27]丁卫平,顾卫江,董建成,祁恒,模糊逻辑推理在电子病历智能辅助诊断系统中的应用研究,南通大学学报(自然科学版)Dem.2006, Vol.5 No.4
[28]赵小芳,张勇,一种动态模糊逻辑程序设计语言[J],计算机工程,2007 Vol.32 No.6
[29]钱剑飞,何钦铭,陈华,俞璃争,一种基于模糊推理的细匹配方法,计算机工程,April 2007Vol.33 No.8[J]
[30]基于本体的中医舌诊知识的获取[J],曹宇峰,曹存根,计算机应用研究,2005.1
[31]王睿,杜静,何玉林,杨显刚,一种模糊知识库系统及其推理机制研究[J],计算机技术与发展,Mar.2007 Vol.17 No.3
[32]王海涛,曹存根,高颖,基于领域本体的半结构化文本知识自动获取方法的设计和实现[J],计算机学报vol.28,2005
[33]石磊,沈超,语义Web：服务描述框架研究综述[J],计算机技术与发展2006
[34]陈再旺,陈景长,一个医疗辅助诊断专家系统的设计与实现[J],计算机系统应用,2001,12
[35]林媛,陈新,崔智.面向对象的医疗诊断推理机设计[J].计算机应用与软件,2001,18(1)：5-9,42.
[36]赵卫东,盛昭瀚.基于形象思维的医疗诊断系统研究[J].系统工程理论与实践,2000,20(10)：108-113,
[37]张红梅,王永成.一个仿人疾病诊断专家系统模型[J].计算机应用研究,2000,17(1)：41-43.
[38]樊永正.模仿思维的医学专家系统[J].计算机研究与发展,1995,32(4)：62-65.
[39]张立群,李杰.一种应用于多专家会诊系统的调度专家算法[J].计算机应用研究,2000,17(3)：12-14.
[40]梁嘉骅,王双惠,李常洪.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,14(1)：83-90.
[41]花蕾.基于知识的肺癌早期细胞诊断系统[J].计算机应用研究,2000,17(2)：90-92.
[42]范逢曦,张海,卢轶郎等.急性心肌梗塞急性期预后专家系统的研究[J].中国生物医学工程学报,1992,11(1)：9-16.
[43]刘自伟.常见内科疾病中医诊疗专家辅助系统的设计及其实现[J].计算机时代,1994,(1)：1-6.
[44]Hudson,医学专家系统中的模糊逻辑[J].国外医学生物医学工程分册,1995,18(3)：148-154.
[45]徐宁,王宽全,张大鹏.基于神经网络的掌纹诊病专家系统[J].计算机应用研究,2001,18(2)：4-6.
[46]林东,邵军力.医学诊疗领域通用专家系统设计与实现[J].自动化学报,1995,21(3)：380-382.
[47]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报,2000,30(3)：46-50.
[47]梁嘉骅,王双惠,等.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,(1)：83-89
[48]龙硕柱,马光志,赵杰.医疗智能诊断系统的实现[J].计算机辅助工程,2003,(2)：75-79
[49]孙佰清,潘启树,等.医疗诊断系统专家知识的表达与获取方法[J].哈尔滨工业大学学报,2001,(1)：134-136
[50]邵虹,崔文成,等.医疗诊断专家系统研究进展[J].小型微型计算机系统,2003,(3)：509-512
[51]王海涛,曹存根,高颖.基于领域本体的半结构化文本知识自动获取方法的设计和实现[J].计算机学报,2005(12)：2010-2018
[52]龙硕柱,马光志,赵杰,医疗智能诊断系统的实现[J],计算机辅助工程,2003.2
[53]侯秀萍,袁秀丽,姜卓等,模糊逻辑技术在医学诊断中的应用研究[J],2005.3
[54]邵虹,崔文成,张继武等,医疗诊断专家系统研究进展[J],小型微型计算机系统,2003Vol.24 No.3
[55]赵卫东,盛昭瀚,杜雪寒,基于神经网络的案例推理医疗诊断[J],东南大学学报,2000Vol.30 No.3
[56]钱宗才,屈景辉,刘敬华等,专家系统知识表示及其在医疗诊断系统中的应用[J],医疗卫生装备,2003.10
[57]柳彦平,王文杰,荣江,基于RDF的医疗诊断专家系统[J],应用奇葩,2005.5
[58]陈铮,医疗诊断专家系统MDESY的设计与实现,微处理机,1996.3
[59]陈真诚,蒋勇,胥明玉等,人工智能技术及其在医学诊断中的应用及发展[J],生物医学工程学杂志,2002 Vol.19 No 3
[60]孙佰清,潘启树,冯英浚等,医疗诊断系统专家知识的表达与获取方法[J],哈尔滨工业大学学报,2000 Vol.33 No.1
[61]黄河清,神经网络辅助医学诊断系统的设计与实现[J],漳州职业大学学报,2002.3
[62]吴燕萍,闫强,计算机辅助医学诊断的理论模型及推理方法[J],2000 Vol.31 No.4
[63]陈再旺,陈景长,一个医疗辅助诊断专家系统的设计与实现[J],2001.12
[64]高黎,卜淮原,胡曙,一种医疗智能诊断推理机的设计与实现[J],2005.1
[65]侯秀萍,袁秀丽,姜早等,不确定性推理技术在医学诊断中的应用研究[J],计算机工程与应用,2005.14
[66]张勇,赵振杰,张德新,数据挖掘及其在医学中的应用[J],西北医学教育,2005 Vol.13 No.3
[67]陆汝钤,金芝,陈刚.面向本体的需求分析[J],软件学报,2000,11(8)：1009～1017.
[68]陈刚,陆汝钤,金芝.基于领域知识重用的虚拟领域本体构造[J],软件科学,2003；(3)
[69]宋红,林家瑞,医学诊断专家系统进展[J],国外医学生物医学工程分册,1995 Vol.18No.3
[70]姚一波,王纪亮,医疗诊断系统专家知识的表达与获取方法[J],信息技术,2002.2
[71]邱银安,林小红,一种病理诊断知识获取RS混合算法[J],科技通报,2005 Vol.21 No.3
[72]章沛,基于数据挖掘技术的青少年健康心理状况研究[J],科教兴市,2005.2
[73]金芝,基于本体的自动需求获取[J],计算机学报,2000,23(5)：486～492.
[74]杨叔子,郑晓军,人工智能与诊断专家系统[J],西安：西安交通大学出版社,1990.
[75]尹朝庆,尹皓,人工智能与专家系统[J],北京,中国水利出版社,2001
[76]俞思伟,医学专家系统的设计原理与实现方法[J],医学信息,2002,15(6).346～349
[77]张惠康,钱宗才,屈景辉等,专家系统及其在医疗诊断中的应用[J],第四军医大学学报,2002.23
[78]李锋刚,倪志伟,郜峦,案例推理技术在医学诊断专家系统中的设计思路探讨[J],中医药临床杂志,2005 Vol.17 No.2
[79]梁嘉骅,王双惠,李常洪等,医疗诊断专家系统开发的新思想与新方法[J],系统工程学报,1999 Vol.14 No.1
[1]Daniel Jurafsky & James H.Martin. Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition[J]. Publishing House of Electronics Industry,2005
[2]Peh Li Shiuan, Christoppher Ting Hian Ann. A Divide-Conquer Strategy for Parsing[J]. In:Proceedings of the ACL/SIGPARSF 5th Inernational Workshop on Parsing Technologies,1996
[3]Abney S. Parsing by chunks[J]. In:Berwick R, Abney S, Tenny C et al. Principle-BasedParsing. Dordrecht:Kluwer Academic Publishers,1991
[4]Steven Abney. Parsing by chunks[J]. In:Robert Berwick, Steven Abney, Carol Tenny. Principle-Based Parsing. Dordrecht. Kluwer Acahemic Publishers,1991
[5]Michael Bieberl, Ricki Goldman-Segall, Towards Knowledge-Sharing and Learning in Virtual Professional Communities[J], System Sciences,2002.35
[6]Noushin Ashrafi, Peng Xu, Jean-Pierre Kuilboer, Boosting Enterprise Agility via IT Knowledge Management Capabilities[J], System Sciences,2006.39
[7]Susan Gasson, Boundary-Spanning Knowledge-Sharing In E-Collaboration[J], System Sciences,2005.38
[8]Daniel D. Suthers, Collaborative Knowledge Construction through Shared Representations[J], System Sciences,2005.38
[9]Yannis Labrou, Tim Finin and Yun Peng, The Interoperability Problem:Bringing together Mobile Agents and Agent Communication Languages[J], System Sciences,1999.32
[10]Peter F. Patel-Schneider, The Semantic Web and Knowledge Representation[J], Computer Science,2004.5
[11]CHAU-YOUNG IVAN LIN, and CHENG-SEEN HO, An Ontology-Based Approach to Acquiring [J], Domain Knowledge for Requirement Analysis, Vol.24, No.1,2000.
[12]Fons Wi jnhoven, Edwin van den Belt, Eddy Verbruggen, Internal Data Market Services: An Ontology-Based Architecture and Its Evaluation[J], Informing Science,2003.7
[13]Geore F. Luger, Artificial Intelligence:Structures and Strategies for Complex Problem Solving, Pearson Education Limited,2002
[14]Minsky, M., A Framework for representing knowledge[J]. In Brachman and Levesque(1985),1975
[15]GENG Weidong, PAN Ynube, Fractal measure theory for knowledge representation[J], 1996 Vol.39 No.4
[16]CAO Cungen, Liveness characterization for FFC systems[J], SCIENCE IN CHINA Vol. 39 No.2,1996
[17]林媛,陈新,崔智.面向对象的医疗诊断推理机设计[J].计算机应用与软件,2001, 18(1)：5-9,42.
[18]樊永正.模仿思维的医学专家系统[J].计算机研究与发展,1995,32(4)：62-65.
[19]张红梅,王永成.一个仿人疾病诊断专家系统模型[J].计算机应用研究,2000,17(1)：41-43.
[20]赵卫东,盛昭瀚.基于形象思维的医疗诊断系统研究[J].系统工程理论与实践,2000,20(10)：108-113.
[21]张立群,李杰.一种应用于多专家会诊系统的调度专家算法[J].计算机应用研究,2000,17(3)：12-14.
[22]Hudson,医学专家系统中的模糊逻辑[J].国外医学生物医学工程分册,1995,18(3)：148-154.
[23]花蕾.基于知识的肺癌早期细胞诊断系统[J].计算机应用研究,2000,17(2)：90-92.
[24]范逢曦,张海,卢轶郎等.急性心肌梗塞急性期预后专家系统的研究[J].中国生物医学工程学报,1992,11(1)：9-16.
[25]徐宁,王宽全,张大鹏.基于神经网络的掌纹诊病专家系统[J].计算机应用研究,2001,18(2)：4-6.
[26]刘自伟.常见内科疾病中医诊疗专家辅助系统的设计及其实现[J].计算机时代,1994,(1)：1-6.
[27]林东,邵军力.医学诊疗领域通用专家系统设计与实现[J].自动化学报,1995,21(3)：380-382.
[28]梁嘉骅,王双惠,李常洪.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,14(1)：83-90.
[29]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报,2000,30(3)：46-50.
[30]梁嘉骅,王双惠,等.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,(1)：83-89
[31]龙硕柱,马光志,赵杰.医疗智能诊断系统的实现[J].计算机辅助工程,2003,(2)：75-79
[32]孙佰清,潘启树,等.医疗诊断系统专家知识的表达与获取方法[J].哈尔滨工业大学学报,2001,(1)：134-136
[33]邵虹,崔文成,等.医疗诊断专家系统研究进展[J].小型微型计算机系统,2003,(3)：509-512
[34]郭茂祖,孙华梅,黄梯云,专家系统中知识库组织与维护技术的研究高技术通讯[J],2002,02
[35]王海涛,曹存根,高颖.基于领域本体的半结构化文本知识自动获取方法的设计和实现[J].计算机学报,2005(12)：2010-2018
[36]邓志鸿,唐世渭等,Ontology研究综述[J],北京大学学报,2002,9
[37]郑家三吉增涛王玉珠刘云,羊病诊断专家系统知识获取、知识表示的研究[J],2002,9
[38]张德海NKI国家和地区地理知识的获取与分析[J],2001
[39]黄荣怀李茂国沙景荣知识工程学：一个新的重要研究领域[J],2002
[40]赵铁军,机器翻译原理[M],哈尔滨工业大学出版社,2000年
[41]夏幼明等,基于非单调推理的领域专家知识库的研究[J],计算机科学,Vol.28 No.9,2001
[42]曹存根,国家基础设施的意义[J],中国科学院院刊,Vol.4,2001
[43]陆汝钤,世纪之交的知识工程与知识科学[J],清华大学出版社,2001年
[44]皮连生,知识分类与目标导向教学[J],华东师范大学出版社,1998年
[45]王永庆,人工智能原理与方法[M],西安交通大学出版社,1998年
[46]M. W. Eysenck (1990), The Blackwell Dictionary of Cognitive Psychology
[47]郭强,知识与社会的互动机制：社会知识化进程,http://www.modernization.com. cn/ guoql2.htm
[48]西安交通大学诊断与控制研究所编,知识工程的应用——从数据到知识[J],2000年8月http://www. monitoring. com.cn/ckddforum/zsgc. htm
[49]徐振宁,黄凯歌等,Ontology建模方法研究[J],计算机科学,2002 Vol..29 No.1
[50]王丽丽,曹存根等,基于本体论的民族知识获取和分析[J],计算机科学：2003 Vo l.30No.5
[51]周肖彬,曹存根等,基于本体的医学知识获取[J],计算机科学,2003 Vol..30 No.10
[52]王礼春,魏祥云,基于规则框架知识表示的配船专家系统[J],决策与决策支持系统,1996 Vol..6 No.1
[53]娄臻亮,张永清,阮雪榆,工程设计KEE系统[J]：知识处理技术,机械科学技术,2001Vol..20 No.4
[54]李龙澍,程慧霞,一种面向对象规则框架知识表示的研究与实践[J],小型微型计算机系统,1997 Vol.18 No.9
[55]邓志鸿,唐世渭,张铭等,Ontology研究综述[J],北京大学学报,2002 Vol.38 No.5
[56]杨艳,李健中,高宏,数字图书馆系统中基于Ontology的用户偏好模型[J],软件学报2005 Vol.16,No.12
[57]吴强,刘宗田,强宇,基于本体的知识库推理研究[J],计算机应用研究,2005
[58]岳静,张自力,本体表示语言研究综述[J],计算机科学,2006 Vol.33 No.2
[59]张东民,廖文和,胡建等,基于本体的设计知识建模[J],华南理工大学学报,2005 Vol.33 No.5
[60]眭跃飞,高颖,曹存根,NKI中的本体、框架和逻辑理论[J],软件学报,2005 Vol.16No.12
[61]张维明,宋峻峰,面向语义Web的领域本体表示、推理与集成研究[J],计算机研究与发展,2006 Vol.43 No.1
[62]周文,刘宗田,陈慧琼,FCA与本体结合研究的综述[J],计算机科学,2006 Vol.33 No.2
[63]雷玉霞,曹宝香,王书西,基于Ontology的概念联通在查询系统中的应用研究[J],计算机科学,2005 Vol.32 No.9
[64]吴江,赵宗涛,基于本体的非结构化知识库系统研究[J],计算机科学,2005 Vol.32No.9
[65]李善平,尹奇华,胡玉杰等,本体论研究综述[J],计算机研究与发展,2004 Vol.41 No.7
[66]徐长江,张优云,顾红亮,基于分层结构的汽轮机故障远程诊断的研究[J],计算机应用,2004 Vol.24
[67]孙海涛,宋荣兴,企业管理信息系统建设的框架手探析[J],石家庄经济学院学报,2000Vol.23 No.4
[68]王浩宇,蔡瑞英,一种面向对象推理模型及其知识表示[J],南京工业大学学报,2002 Vol.24 No.3
[69]规则框架的一种实用简化计算方法[J],广州大学学报,2001 Vol. 15 No.8
[70]龚元明,王小娟,树结构与框架推理[J],指挥技术学院学报,2000 Vol.11 No.5
[71]刘海龙,马孝江,基于框架理论的三相异步电动机故障诊断专家系统[J],机械工程师,2002.5
[72]付炜,地理专家知识表示的框架网络模型研究[J],地理研究,2002 Vol.21 No.3
[73]董明,查建中,杜玉明等,用面向对象的框架语言实现离散事件仿真[J],天津大学学报,1997 Vol.30 No.1
[74]于中华,唐常杰,张天庆,自然语言句法结构的框架树表示方法[J],小型微型计算机系统,1999 Vol.20 No.8
[75]朱绍文,张大斌,吕少鹏等,商业MIS框架设计辅助专家系统的知识处理方法[J],计算机工程与设计,1999 Vol.20 No.4
[76]蔡智明,刘宗田,黄自强,Web上分布式领域组件的框架表示[J],微电子学与计算机,1999.4
[77]何昭,李传湘,崔巍,基于面向对象框架的软件开发方法[J],计算机工程,2002Vol..28 No.4
[78]梁怡,人工智能、空间分析与空间决策[J],地理学报,1997 Vol.52 Supplement
[79]李莉, 欧灵,时态框架的推理策略[J],西南师范大学学报,1998 Vol.23 No.6
[80]朱绍文,吕少鹏,张大斌等,商业MIS框架生成系统的知识表示研究[J],小型微型计算机系统,1999 Vol.20 No.12
[81]于中华,唐常杰,张天庆等,一种用于建立句法结构框架树的有效分析算法[J],小型微型计算机系统,1999 Vol.20 No.9
[82]黄素英,王周敬,面向对象框架在信息系统开发中的应用[J],合肥工业大学学报,2003Vol.26 Supplement
[83]周晓庆,知识管理系统和CIMS系统的构建与设计[J],计算机应用,2005 Vol.25 No.9
[84]张志勤,李强,基于hibernate的知识库管理系统的开发[J],计算机与信息技术,2006
[85]张再跃,眭跃飞,曹存根,基于模糊命题模态逻辑的形式推理系统[J],软件学报,2005Vol.16 No.8
[86]钟诗胜,傅万涛,周济等,面向对象的模糊知识表达及推理[J],南昌大学学报,1995 Vol.19 No.3
[87]王拥军,倪平,王翠茹,缺省推理中的模糊知识[J],西安工业学院学报,2001 Vol.21No.1
[88]周大强,刘云光,加权模糊逻辑推理及在医学诊断专家系统中的应用[J],青海大学学报,1994 Vol.12 No.1
[89]凌云,基于多种知识表示下的常规推理机的设计与实现[J],微计算机应用,1996 Vol.17 No.3
[90]程晓春,刘叙华,基于模糊逻辑的不确定知识处理[J],计算机学报,1996 Vol.19 No.12
[91]曹存根,国家知识基础设施的意义[J],中国科学学院院刊,2001.4
[92]冯东辉,曹存根,知识界面在NKI中的应用[J],计算机工程与应用,2002.17
[93]秦前付,曹存根,安鹏等,基于装备水平和知识的空中作战效果分析[J],火力与指挥控制,2006.1
[94]秦前付,徐恍,曹存根,作战计划策略的表示与应用[J],系统工程理论与实践,2005.6
[95]沈一栋,知识工程.北京：科学技术出版社,1992
[96]唐素勤,刘立浩,曹存根,智能教学系统NKI-Tutor的教学策略研究[J],广西师范大学学报,2003 Vol.21 No.2
[97]曹存根,从专家分析实例中学习知识[J],软件学报,1994 Vol.5 No.6
[98]季秋,王万森,马建红,人工智能科学中的概率逻辑[J],计算机应用与软件,2006,Vol.23 No.1
[100]周涛,Apriori算法优化策略[J],福建电脑2006年第10期。
[101]娄兰芳,潘庆先,关联规则挖掘的一个高效预处理算法[J],烟台大学学报(自然科学与工程版)2007年1月
[102]曾舸,刘先锋关联规则挖掘中Apriori改进算法的研究[J],2007年第1期计算机与现代化
[103]孙志强,基于FP—Growth的入侵检测研究[J],计算机与通信工程学院,计算机技术与发展2006年12月
[104]张世海,李建国,宋健民,李超,基于改进Apriori算法的高层结构智能选型知识发现[J],2006年第6期总第91期建筑管理现代化
[105]吴沛,粟湘,基于关联规则挖掘的科技论文引文分析—以化学领域科技期刊为例[J],情报学报ISSN1000—0135第25卷第6期643—650
[106].张博,张虹,基于关系数据库的关联规则的形式化开采[J],第27卷 V0l.27第24期N0.24计算机工程与设计
[107]吴佳英,李平,郑金华,李少年基于兴趣度的时态关联规则挖掘算法[J],2006年第6期
[108]江彬,张学习,数据挖掘技术在现代化图书馆中的应用[J],情报探索2007年1月
[109]庞洁,率睿仙,胡建华,数据挖掘在电信交叉销售领域的研究[J],云南省昆明理工大学学报2006年第8期
[110]周翠红,贺建军,挖掘关联规则中对Apriori算法的一个改进[J],第15卷第4期2006年12月湖南城市学院学报(自然科学版)
[111]朱孝宇,王理冬,汪光阳,一种改进的Apriori挖掘关联规则算法[J],第2006年06期计算机技术与发展
[112]张永,迟忠先,一种高效的基于采样的关联规则挖掘算[J],计算机工程与应用,第11卷第4期2006年12月
[113]李环宇,杜春玲,李保银,一种基于关联规则挖掘的改进算法[J],2007年第1期 福建电脑
[114]朱可,胡克瑾,一种基于模糊关联规则挖掘的攻击识别系统[J],计算机科学2006Vol.33No.12(增刊)
[115]李陶深, 李新仕,一种新的基于投影的频繁模式树构造算法[J],计算机科学2006Vol.33No.12(增刊)
[116]赵海丰,邢永康,杨华丽,秦鹏,一种用于挖掘正、负关联规则的改进Apriori算法[J],计算机科学 2006Vol.33No_.12(增刊)
[117]黄勤,贺向前,刘益良,姚雪梅,遗传算法和关联规则算法用于入侵检测系统的研究[J],情报探索2007年1月
[118]任志波,遗传优化模糊约束的频繁项集挖掘[J],第26卷第10期2006年1O月北京理 工大学学报
[1]Borst W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis [J], University of Twente, Enschede,1997
[2]Kai Mertins, Peter Heisig, Jens Vorbeck et.al. Knowledge Management Concepts and Best Practices [J].Springer-Verleg Berlin Heidelbeg New York,2003
[3]Studer R, Benjamins VR, Fensel D. Knowledge Engineering, Principles and Methods [J].Data and Knowledge Engineering,1998,25 (122):161～197
[4]Neches R, Fikes RE, Gruber TR, et al. Enabling Technology for Knowledge Sharing [J]. AIMagazine,1991,12 (3):36～56
[5]Daniel Jurafsky & James H. Martin. Speech and Language Processing:An Introduction to Natural Language Processing [J], Computational Linguistics, and Speech Recognition. Publishing House of Electronics Industry,2005
[6]Gruber T R. A Translation Approach to Portable Ontology Specifications [J]. Knowledge Acquisition,1993,5:199～220
[7]Guarino N. Semantic Matching:Formal Ontological Distinctions for Information Organization, Extraction, and Integration [J]. In:Pazienza MT, eds. Information Extraction:A Multidisciplinary Approach to an Emerging Information Technology, Springer Verlag,1997,139～170
[8]Uschold M. Building Ontologies:Towards A Unified Methodology. In expert systems 96,1996 Wordnet. Http://www. Cogsci. Princeton. Edu Framenet. Http://www. Icsi. Berkeley. Edu
[9]Gruber T R. Towards Principles for the Design of Ontologies Used for Knowledge Sharing [J]. International Journal of Hu2man2Computer Studies,1995, 43:907-928
[10]Guarino N, Welty C. A Formal Ontology of Properties. In:Dieg R, Corby O, eds. The Proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management (EKAW'2000), [J], Springer Verlag,2000,97～112
[11]Guarino N, Masolo C, Vetere G. OntoSeek:Content2Based Access to the Web [J]. IEEE Intelligent Systems,1999,14 (3):70～80
[12]Perez AG, Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods.Workshop on Ontologies and Problem2Solving Methods:Lessons Learned and Future Trends (IJCAI99), deAgosto, Estocolmo,1999 [Perez,1999] Perez AG, Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods. In: Stockholm VR, Benjamins B, Chandrasekaran A, eds. Proceedings of the IJCAI299 workshop on Ontologies and Problem2Solving Methods (KRR5) 1999,1～15
[13]Shun SB, Motta E, Domingue J. ScholOnto:an Ontology2based Digital Library Server for Research Documents and Discourse [J]. Intl J Digital Libraries,2000,3 (3): 237-248
[14]Bhatl CD.Mangement strategies for individual knowledge and organizational knowledge [J]. JKnowledgeManagement,2002,6 (1):31-39
[15]Chandra S. Amaravadi, The Dimensions of Process Knowledge [J], Knowledge and Process Management,2005; 12 (1)
[16]Injun Choi, Jisoo Jung, Minseok Song. A Framework for the Integration of Knowledge Management and Business Process Management [J]. Int. J. Innovationand Leaning,2004; (4)
[17]of the pay-jun, Shan-Ping Li, Guo Mingfang, product data model of the body of knowledge [J], computer-aided design and graphics Journal, Mar,2005, vol.17 NO.3
[18]Wu Pei Road, on fuzzy logic and fuzzy logic based reasoning of the Decade Review [J], the Journal of Mathematics,
[19]Gu Jin Rui, Wang Fang on the ontology of the Review of Information Science June,2007, Vol,25, No.6 [J]
[20]Xia Jin-Wen XML-based body of knowledge modeling language Jiujiang College Journal (Natural Science) June,2003, No.6 [I]
[21]Fu Zetian, Guo Yonghong, Ao Limin, the body of knowledge-based diagnostic reasoning integrated model of agricultural systems science and comprehensive study [J], May,2004, Vol.20, No.2
[22]Zhao Bo, David Xia Ming, Wang Yong, Ji-li solution, based on the object of the body design [J], knowledge base of Yunnan Normal University Journal, Apr.2004Vol.2 No.2
[23]Liu Xiaohui, Tong Weiguang, Shu Lin Kuan, based on fuzzy reasoning Expert System Research and Implementation [J], Apr.2007Vol.3 NO.2
[24]Ke Xu Xiang, Zhang Yousheng, case-based reasoning of the structural design of stamping die in the framework of knowledge [J], computer engineering and application,2002.13
[25]on grounds that Liu would like, Li Mei-ting, continuous production line equipment fault diagnosis expert system for dynamic mechanism of fuzzy inference [J], in June 2005 Vol.7 NO.6
[26]Lei Yuan, and the domain knowledge of the XML body of knowledge model that framework [J], computer engineering, January 2006
[27]Ding Weiping, Gu Wei Jiang, Mr Tung Chee-chen, QI Heng, fuzzy logic in support of intelligent electronic medical records system of research [J], Nantong University Journal (Natural Science) Dem.2006, Vol.5 No.4
[28]Zhao Xiaofang, Zhang Yong, a dynamic fuzzy logic programming language computer engineering [J],2007 Vol.32 No.6
[29]money Jianfei, He Qin-ming, Chen Hua, Yu Li dispute over a fine based on fuzzy matching method of reasoning [J], computer engineering, April 2007 Vol.33 No.8 [J]
[30]Cao Yufeng, Cao stubs,based on the Chinese tongue inspection body of knowledge acquisition [J],, computer application research,2005.1
[31]Wang Rui, Du Jing and Yu-Lin He, Yang was just, a fuzzy knowledge base system and its mechanism of reasoning [J], computer technology and development, Mar.2007 Vol.17 No.3
[32]Wang Haitao, Cao stubs, Gao Ying, Based on the field of semi-structured body of the text automatically acquire knowledge of the design and implementation methods [J], computer Journal vol.28,2005
[33]Shi Lei, Shen Chao, Semantic Web:Review of the framework described services [J], computer technology and development,2006
[34]a medical diagnosis expert system design and implementation, re-Wang Chen, Chen Jing Long, computer applications,2001,12
[35]Lin Yuan, Chen, Cui. Object-oriented medical diagnostic reasoning machine design [J]. Computer application and software,2001,18 (1):5-9,42.
[36]Zhao, Sheng Zhaohan. Image of thinking based on the medical diagnosis system [J]. Systems engineering theory and practice,2000,20 (10):108-113.
[37]Zhang Hongmei, Wang Yongcheng. A humanoid disease diagnosis expert system model [J]. Computer Application Research,2000,17 (1):41-43.
[38]FAN Yong-being. Imitate thinking of the medical expert system [J]. Computer Research and Development,1995,32 (4):62-65.
[39]Zhang Liqun, Li Jie. A more expert diagnosis system for the dispatching of experts algorithm [J]. Computer Application Research,2000,17 (3):12-14.
[40]LIANG Jia-hua, Wang double-hui, Lee Hung. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal, 1999,14 (1):83-90.
[41]bud. Knowledge-based cell lung cancer early diagnosis system [J]. Computer Application Research,2000,17 (2):90-92.
[42]on Fan Xi, Zhang Hai, Lu Yi, such as Lang. Acute phase of acute myocardial infarction prognosis expert system of [J]. Chinese Journal of Biomedical Engineering, 1992,11 (1):9-16.
[43]Liu Wei. Diseases common medical clinics of Chinese medicine experts support the design and realization of [J]. The computer age,1994, (1):1-6.
[44]Hudson, medical experts in the fuzzy logic system [J]. Abroad medical biomedical engineering division,1995,18 (3):148-154.
[45]Xu Ning, Wang Kuan-wide, Da-Peng Chang. Palmprint based on neural network Diagnosis Expert System [J]. Computer Application Research,2001,18 (2):4-6.
[46]Dong, Shao Jun force. Generic field of medical clinics Expert System Design and Implementation [J]. Automation Journal,1995,21 (3):380-382.
[47]Zhao, Zhao-Han Sheng, DU Xue Han. Based on neural network of case-based reasoning medical diagnosis [J]. Journal of Southeast University,2000,30 (3):46-50.
[47]LIANG Jia-hua, Wang Hui-double, and so on. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal, 1999, (1):83-89
[48]Long-seok-chu, Ma Guang Zhi, Zhao Jie. Medical diagnosis system of [J]. Computer-aided engineering,2003, (2):75-79
[49]Sun Bai Qing, PAN Qi Shu, etc. medical diagnostic systems expert knowledge of expression and access methods [J]. Harbin Institute of Technology Journal,2001, (1):134-136
[50]Shaohong, Cui Wencheng, etc. medical diagnostic expert system Progress [J]. Small micro-computer systems,2003, (3):509-512
[51]Wang Haitao, Cao stubs, high-ying. Ontology based on the field of semi-structured text automatically acquire knowledge of methods of design and implementation of [J]. Computer Journal,2005 (12):2010-2018
[52]Long-seok-chu, Ma Guang Zhi, Zhao Jie, medical diagnosis system for smart [J], computer-aided engineering,2003.2
[53]Hou Xiuping, Yuan beautiful, Jiang Zhuo, fuzzy logic technology in medical diagnosis of applied research [J],2005.3
[54]Shaohong, Cui Wencheng, Ji-Wu Zhang, medical diagnostic expert system research progress [J], small micro-computer systems,2003 Vol.24 No.3
[55]Zhao, Zhao-Han Sheng, DU Xue Han, based on neural network of case-based reasoning medical diagnosis [J], the Journal of Southeast University,2000 Vol.30 No.3
[56]Qujing Hui, Liu Jinghua,money in it, expert system that knowledge in the medical diagnostic system and its application in medical and health equipment [J], 2003.10
[57]Liu Yanping, Wang Wenjie, Rongjiang, based on the RDF medical diagnosis expert systems [J], applications wonderful work,2005.5
[58]Chen Zheng, medical diagnostic expert system MDESY the design and implementation [J], the microprocessor,1996.3
[59]Chen sincere, Jiang Yong, Xu Ming-yu, and its artificial intelligence technology in medical diagnosis of and development [J] of biomedical engineering magazine, 2002 Vol.19 No 3
[60]Sun Bai Qing, PAN Qi Shu, Ying Feng Zhijun, medical diagnostic systems expert knowledge of expression and access methods, Harbin Institute of Technology Journal,2000 Vol.33 No.l
[61]-the Yellow River, neural network-assisted medical diagnosis system design and implementation [J], Zhangzhou Vocational University Journal,2002.3
[62]Wu Yanping, Yan-keung, computer-aided medical diagnosis of the theoretical models and reasoning [J],2000 Vol.31 No.4
[63]re-Wang Chen, Chen Jing Long, a medical diagnosis expert system design and implementation [J] 2001.12
[64]High Lebanon, the BU Huai, Hu Shu, a medical diagnostic reasoning Intelligent Design and Implementation of the machine [J],2005.1
[65]Hou Xiuping, Yuan beautiful, as early as ginger, uncertainty reasoning technology in medical diagnosis of applied research, engineering and computer applications [J],2005.14
[66]Zhang Yong, Zhao Zhenjie, Zhang Dexin, data mining and its Application in Medicine [J], Northwest Medical Education,2005 Vol.13 No.3
[67]Mr Luk Qian, Jin-zhi, Chen Gang. Bulk of the demand-oriented analysis [J]. Journal of Software,2000,11 (8):1009 to 1017.
[68]Chen Gang, Mr Luk Qian, Jin-zhi. Based on the field of knowledge reuse areas of the virtual body structure [J]. Software science,2003; (3)
[69]Song Hong, Lin Jia-rui, the medical diagnosis expert system progress, and foreign medical bio-medical engineering division [J],1995 Vol.18 No.3
[70]wave of Yao, Wang Ji-liang, medical diagnostic imaging expert knowledge of expression and access methods [J], information technology,2002.2
[71]Chiu Yin-an, LIN Xiao-hong, a pathological diagnosis RS mixed method of knowledge acquisition [J], technology communications,2005 Vol.21 No.3
[72]Zhang Pei, based on data mining techniques of psychological status of adolescent health research [J], the city through science and education,2005.2
[73]Jin-zhi, for the bulk of the demand for automatic access [J]. Computer Journal, 2000,23 (5):486-492.
[74]Yang Shuzi, ZHENG Xiao-jun, artificial intelligence and diagnostic expert system [J], Xi'an:Xi'an Jiaotong University Press,1990.
[75]Yin Chao-Ching, Yin Hao, artificial intelligence and expert systems [J], Beijing, China Water Conservancy Press,2001
[76]Yu Siwei, the medical expert system design principles and implementation methods [J], medical information,2002,15 (6).346-349
[77]Zhang Huikang, the money in it, Qujing Hui, expert systems in medical diagnosis and the application of the Fourth Military Medical University Journal [J], 2002.23
[78]Li Fenggang, Ni Zhiwei, Gao Luan, case-based reasoning technology in medical diagnosis expert system in the design of the study [J], in the Clinical Journal of Medicine,2005 Vol.17 No.2
[79]LIANG Jia-hua, Wang double-hui, Li Hung Chang, medical diagnostic expert system developed by new ideas and new methods [J], systems engineering Journal, 1999 Vol.14 No.1
[1]Daniel Jurafsky & James H.Martin. Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Publishing House of Electronics Industry,2005
[2]Peh Li Shiuan, Christoppher Ting Hian Ann. A Divide-Conquer Strategy for Parsing. In: Proceedings of the ACL/SIGPARSF 5th Inernational Workshop on Parsing Technologies,1996
[3]Abney S. Parsing by chunks. In:Berwick R, Abney S, Tenny Cetal. Principle-BasedParsing. Dordrecht:Kluwer Academic Publishers,1991
[4]Steven Abney. Parsing by chunks. In:Robert Berwick, Steven Abney, Carol Tenny. Principle-Based Parsing. Dordrecht. Kluwer Acahemic Publishers,1991
[5]Michael Bieberl, Ricki Goldman-Segalll, Towards Knowledge-Sharing and Learning in Virtual Professional Communities, System Sciences,2002.35
[6]Noushin Ashrafi, Peng Xu, Jean-Pierre Kuilboer, Boosting Enterprise Agility via IT Knowledge Management Capabilities, System Sciences,2006.39
[7]Susan Gasson, Boundary-Spanning Knowledge-Sharing In E-Collaboration, System Sciences,2005.38
[8]Daniel D. Suthers, Collaborative Knowledge Construction through Shared Representations, System Sciences,2005.38
[9]Yannis Labrou, Tim Finin and Yun Peng, The Interoperability Prob]em:Bringing together Mobile Agents and Agent Communication Languages, System Sciences,1999.32
[10]Peter F. Patel-Schneider, The Semantic Web and Knowledge Representation, Computer Science,2004.5
[11]CHAU-YOUNG IVAN LIN, and CHENG-SEEN HO, An Ontology-Based Approach to Acquiring Domain Knowledge for Requirement Analysis, Vol.24, No.1,2000.
[12]Fons Wijnhoven, Elwin van den Belt, Eddy Verbruggen, Internal Data Market Services: An Ontology-Based Architecture and Its Evaluation, Informing Science, 2003.7
[13]Geore F. Luger, Artificial Intelligence:Structures and Strategies for Complex Problem Solving, Pearson Education Limited,2002
[14]Minsky, M., A Franework for reprosenting knowledge. In Brachman and Levesque(1985),1975
[15]GENG Weidong, PAN Ynube, Fractal measure theory for knowledge representation,1996 Vol.39 No.4
[16]CAO Cungen, Liveness characterization for FFC systems, SCIENCE IN CHINA Vol.39 No.2,1996
17] Lin Yuan, Chen, Cui. Object-oriented medical diagnostic reasoning machine design [J]. Computer application and software,2001, 18 (1):5-9,42.
[18]FAN Yong-being. Imitate thinking of the medical expert system [J]. Computer Research and Development,1995,32 (4):62-65.
[19]Zhang Hongmei, Wang Yongcheng. A humanoid disease diagnosis expert system model [J]. Computer Application Research.2000,17 (1):41-43.
[20]Zhao, Sheng Zhaohan. Image of thinking based on the medical diagnosis system research [J]. Systems engineering theory and practice,2000,20 (10):108-113.
[21]Zhang Liqun, Li Jie. A more expert diagnosis system for the dispatching of experts algorithm [J]. Computer Application Research, 2000,17 (3):12-14.
[22]Hudson, medical experts in the fuzzy logic system [J]. Abroad medical biomedical engineering division,1995,18 (3):148-154.
[23]bud. Knowledge-based cell lung cancer early diagnosis system [J]. Computer Application Research,2000,17 (2):90-92.
[24]on Fan Xi, Zhang Hai, Lu Yi, such as Lang. Acute phase of acute myocardial infarction prognosis expert system of [J]. Chinese Journal of Biomedical Engineering,1992,11 (1):9-16.
[25]Xu Ning, Wang Kuan-wide, Da-Peng Chang, Palmprint based on neural network Diagnosis Expert System [J]. Computer Application Research,2001, 18 (2):4-6.
[26]Liu Wei. Diseases common medical clinics of Chinese medicine experts support the design and realization of [J]. The computer age,1994, (1): 1-6.
[27]Dong, Shao Jun force. Generic field of medical clinics Expert System Design and Implementation [J]. Automation Journal,1995,21 (3):380-382.
[28]LIANG Jia-hua, Wang double-hui, Lee Hung. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal,1999,14 (1):83-90.
[29]Zhao, Zhao-Han Sheng, DU Xue Han. Based on neural network of case-based reasoning medical diagnosis [J]. Journal of Southeast University,2000,30 (3):46-50.
[30]LIANG Jia-hua, Wang Hui-double, and so on. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal,1999, (1):83-89
[31]Long-seok-chu, Ma Guang Zhi, Zhao Jie, Medical diagnosis system of [J]. Computer-aided engineering,2003, (2):75-79
[32]Sun Bai Qing, PAN Qi Shu, etc. medical diagnostic systems expert knowledge of expression and access methods [J]. Harbin Institute of Technology Journal,2001, (1):134-136
[33]Shaohong, Cui Wencheng, etc. medical diagnostic expert system Progress [J]. Small micro-computer systems,2003, (3):509-512
[34]Guomao Zu, Sun Hua Mei, HUANG Ti-yun, expert systems in the organization and maintenance of technical knowledge base of high-tech communications,2002,02
[35]Wang Haitao, Cao stubs, Gao Ying. Ontology based on the field of semi-structured text automatically acquire knowledge of methods of design and implementation of [J]. Computer Journal,2005 (12):2010-2018
[36]Teng, Tang Wei, and so on, Ontology Review, the Journal of Peking University,2002,9
[37]Andrew Kyrgyzstan by three-Tao Wang Yuzhu Liu, sheep disease diagnosis expert system access to knowledge, knowledge that the study, 2002,9
[38]ZHANG De-hai NKI countries and regions geographical knowledge acquisition and analysis,2001
[39]Huang Ronghuai Mao-Guo Li Sha King-knowledge engineering:a new and important field of research,2002
[40]Zhao Tiejun, machine translation principle [M], Harbin Institute of Technology Press,2000
[41]David Ming Xia, nonmonotonic reasoning based on the knowledge base experts in the field of research, computer science, Vol.28 No.9,2001
[42]Cao stubs, the significance of national infrastructure, the Chinese Academy of Sciences Institute published in, Vol.4,2001
[43]Mr Luk Qian, the turn of the century of knowledge and knowledge of science and engineering, Tsinghua University Press,2001
[44]Paper Lian-sheng, knowledge classification and goal-oriented teaching, East China Normal University Press,1998
[45]Wang Yung-ching, artificial intelligence principles and methods [M], Xi'an Jiaotong University Press,1998
[46]MW Eysenck (1990), The Blackwell Dictionary of Cognitive Psychology
[47]Guo Qiang, knowledge and social interaction mechanism:the process of knowledge-based society, http://www.modernization.com.cn/ guoq12.htm
[48]Xi'an Jiaotong University Institute for diagnosis and control, the application of engineering knowledge-from data to knowledge, in August 2000 http://www.monitoring.com.cn/ckddforum/zsgc.htm
[49]Xu Zhenning, songs of praise, such as yellow, Ontology Modeling studies, computer science,2002 Vol..29 No.1
[50]Wang Lili, Cao stubs and so on, based on the ontology of the national knowledge acquisition and analysis, computer science:2003 Vo 1.30No. 5
[51]Zhou Xiao Bin, Cao stubs and so on, based on the body of medical knowledge acquisition, Computer Science,2003 Vol..30 No.10
[52]Wang Lichun, Wei Xiangyun, rule-based framework with the knowledge that the ship expert system, decision-making and decision support system, 1996 Vol..6 No.1
[53]Zhen-Liang Lou, Zhang Yongqing, Ruan Xueyu, engineering design KEE system:knowledge processing technology, mechanical science and technology,2001 Vol..20 No.4
[54]Lee Yong Shu, Cheng Huixia, an object-oriented framework of rules that knowledge of research and practice, small micro-computer systems, 1997 Vol.18 No.9
[55]Teng, Tang Wei, Zhang Ming, and so on, Ontology Review, the Journal of Peking University,2002 Vol.38 No.5
[56]Yang Yan, Li Jian, Gao Hong, the digital library system based on user preferences Ontology model, Journal of Software 2005 Vol.16, No.12
[57]Wu Qiang, LIU Zong-tian, strong-yu, the knowledge base reasoning Ontology-based research, computer application research,2005
[58]Jing Yue, Zhang Zili, body language said Review, Computer Science, 2006 Vol.33 No.2
[59]Zhang Dong Min, Liao Wen-he, Jian Hu, based on the design of the' Ontology knowledge modeling,South China University of Technology Journal,2005 Vol.33 No.5
[60]Sui Yue Fei, Gao Ying, Cao stubs, NKI in the body, the theoretical framework and logic, software Journal,2005 Vol.16 No.12
[61]Zhang Weiming, Song Junfeng, and the Semantic Web domain ontology said, reasoning and integration research, computer research and development,2006 Vol.43 No.1
[62]Zhou, Zong-Tian Liu, Chen Huiqiong, FCA and the bu.k of the studies reviewed, computer science,2006 Vol.33 No.2
[63]Lei Yuxia, Cao Bao-xiang, Wang West, based on the concept of Unicom Ontology in the query system of applied research, comput er science,2005 Vol.32 No.9
[64]Wu Jiang, Zong-Tao Zhao, the ontology Based unstru:tured knowledge base system, computer science,2005 Vol.32 No.9
[65]Shan-Ping Li, Yinqi China, Hu Yujie, ontology Revi sw, the Computer Research and Development,2004 Vol.41 No.7
[66]Xu Chang jiang, Zhang Youyun, Ou Hongliang, based on the hierarchical structure of the turbine failure of the long-range re earch, computer application,2004 Vol.24
[67]Hai-Tao Sun, Song Rongxing. enterprise managerent information system construction in the frame (?)ork of hand, Shijia huang Institute of Economic Journal,2000 Vol.2(?) No.4
[68]Wang Haoyu, Cai Ruiying, a model and its object-oriented reasoning knowledge that the Nanjing University of Technology Journal,2002 Vol. 24 No.3
[69]a practical framework of rules to simplify the calculation method, the Journal of Guangzhou University,2001 Vol.15 No.8
[70]Gong Yuan-ming, Wang Xiaojuan, structure and framework of the tree reasoning, the command Institute of Technology Journal,2000 Vol.11 No.5
[71]Liu Hailong, MA Xiao Jiang, based on the theoretical framework of the three-phase asynchronous motor fault diagnosis expert system, mechanical engineers,2002.5
[72]Fu Wei, geography expert knowledge that the framework network model, geographic research,2002 Vol.21 No.3
[73]Dong, Zha Jianzhong, Yu-Ming Du, using object-oriented language in the framework of discrete event simulation, Journal of Tianjin University,1997 Vol.30 No.1
[74]in China, Tang Chang Jie, Zhang Qing-day, natural language syntax structure of the framework of the tree that way, small micro-computer systems,1999 Vol.20 No.8
[75]Zhu Shaowen, Zhang Da-bin, Lvshao Peng, commercial MIS framework design expert system for supporting knowledge processing methods, computer engineering and design,1999 Vol.20 No.4
[76]Cai Zhiming, Zong-Tian Liu, Huang Ziqiang, Web Distributed on the field components of the framework, micro-electronics and computers, 1999.4
[77]He Zhao, Li Xiang, Cui Wei, based on object-oriented framework for software development methods, computer engineering,2002 Vol..28 No.4
[78]Liang Yi, artificial intelligence, spatial analysis and decision-making space, geographical Journal,1997 Vol.52 Supplement
[79]Li Li, Europe Ling, temporal reasoning framework of the strategy, Journal of the Southwest Normal University,1998 Vol.23 No.6
[80]Zhu Shaowen, Lvshao Peng, Zhang Da-bin, commercial MIS framework generation system of knowledge that study, small micro-computer systems, 1999 Vol.20 No.12
[81]in China, Tang Chang Jie, Zhang Qing, and other days, the syntactic structure of the establishment of a framework for the effective analysis of tree algorithm, small micro-computer systems,1999 Vol.20 No.9
[82]Huang Su-ying, Wang Zhou Jing, object-oriented framework for the development of information systems applications, Hefei University of Technology Journal,2003 Vol.26 Supplement
[83]Zhou Xiaoqing, knowledge management systems and CIMS system Construction and design, computer application,2005 Vol.25 No.9
[84]Zhi-Qin Zhang, Li Qiang, based on hibernate the knowledge base management system development, computer and information technology,2006
[85]re-yue Zhang, Sui Yue Fei, Cao stubs, based on fuzzy proposition in the form of modal logic reasoning systems, software Journal,2005 Vol.16 No.8
[86]Chung Shi-sheng, Fu million Tao, Zhou Ji, the fuzzy object-oriented knowledge and their reasoning, Journal of Nanchang University,1995 Vol. 19 No.3
[87]Wang Yongjun, Ni Ping, Wang Ru, default reasoning in the fuzzy knowledge, Xi'an Institute of Industrial Journal,2001 Vol.21 No.1
[88]weeks of strong, Liu Yun-kwong, the weighted fuzzy logic and expert systems in medical diagnosis of, Qinghai University Journal,1994 Vol. 12 No.1
[89]Lingyun, a variety of knowledge based on the conventional reasoning that the design and implementation of micro-computer applications,1996 Vol.17 No.3
[90]Xiao-Chun Cheng and Liu, Syria, based on fuzzy logic of uncertainty knowledge processing, computer Journal,1996 Vol.19 No.12
[91]Cao stubs, the state of knowledge of the significance of infrastructure, the Chinese scientific Xueyuanyuankan,2001.4
[92]Ping Donghui, Cao stubs, knowledge interface NKI in the application of engineering and computer applications,2002.17
[93]before the Qin pay stubs Cao, An Peng, equipment and knowledge-based aerial combat effectiveness analysis, firepower and command and control, 2006.1
[94]to pay before the Qin and Xu Huang, Cao stubs, operational planning strategy and that the application of systems engineering theory and practice,2005.6
[95]-1, knowledge engineering. Beijing:Science and Technology Press, 1992
[96]Tang Su-qin, Liuli Hao, Cao stubs, intelligent tutoring system NKI-Tutor of the Study, Journal of Guangxi Normal University,2003 Vol. 21 No.2
[97]Cao stubs from the expert analysis of examples to learn knowledge, software Journal,1994 Vol.5 No.6
[98]JIQIU, Wang Sen, Ma Jian-hong, the probability of artificial intelligence in the logic of science, computer application and software, 2006, Vol.23 No.1
[100]Zhou Tao, Apriori algorithm optimization strategy, Fujian in 2006 the first computer 10.
[101]Lou Lanfang, the first Qing Pan, the Mining Association Rules Pretreatment a highly efficient algorithms, Yantai University Journal (Natural Sciences and Engineering) in January 2007
[102]had Ge, LIU Xian-feng mining association rules in the Algorithm Apriori improve the first study in 2007 with a modern computer
[103]Sun Zhiqiang, based on the FP-Growth of the intrusion detection research, the Computer and Communications Engineering Institute, computer technology and development in December 2006
[104]Zhang Hai, Li Jianguo, Song Min, Li Chao, based on improved algorithms Apriori intelligent selection of high-level knowledge that in 2006 the first section 6 of 91 phase construction management modernization
[105]Wu Pei, Su-hsiang, the mining association rules based on the scientific papers citation analysis-to the field of chemical science and technology periodicals as an example, the intelligence Journal ISSN1000-0135 Volume 25 No.6 643-650
[106]. Zhang Bo, Zhang Hong, the relational database based on the formal mining association rules Vol.27 VO1.27 No.24 NO.24 computer engineering and design
[107]Wu Jiaying, Li Ping, Jin-Hua Zheng, Li juvenile based on the degree of interest when the state mining association rules algorithm,2006 No. 6
[108]Jiang-bin, Zhang learning, data mining technology in the modern library in the application of intelligence to explore in January 2007
[109]Pang Jie, the rate Rui cents, Hu Jianhua, data mining in the telecommunications research in the field of cross-selling, Polytechnic University Journal of Kunming in Yunnan Province in 2006 the first eight
[11]Zhou Cuihong, He Jianjun, the rules of the Mining Association of Apriori an improved algorithm, Vol 15 No.4 December 2006 Journal of Hunan City College (Natural Science)
[111]Zhu Xiaoyu, Wang Dong, Yang Guang, an improved algorithm Apriori Mining Association Rules,2006 06 computer technology and development
[112]Chang, Chi-chung first, based on an efficient sampling of the association rule mining operators, computer engineering a id application of article 11 paragraph 4 volumes in December 2006
[113]Lee Huan, the Du Chunling, Li Yin, a mining association rules based on the improved algorithm,2007 No.1 computer Fujian
[114]Zhu, Hu Ke Jin, a mining association rules based on fuzzy attack identification systems, computer science 2006 Vol.33No.12 (supplement)
[115]Lee Tao-shen, Li Shi, a new projection based on the frequent pattern tree structure algorithms, computer science 2006 Vol.33No.12 (supplement)
[116]Zhao Haifeng, XING Yongjian Kang, Yang gorgeo(?)s, Peng Qin, excavation for a positive and negative association rules to improve Apriori algori(?)hm, computer science 2006 Vol.33No.12 (supplement)
[117]Qin Huang, HE forward, Liu Liang, Yaoxue Mei, genetic algorithms and algorithms for the association rules of intrusion detection systems, intelligence exploration in January 2007
[118]Ren Zhi-bo, genetic optimization of fuzzy constraints of the frequent mining, Vol 26 No.10 October 2006 Beijing Institute of Technology Journal
[3]Kai Mertins, Peter Heisig, Jens Vorbeck et. al. Knowledge Management Concepts and Best Practices. Springer-Verleg Berlin Heidelbeg New York,2003 [J]
[2]Studer R, Benjamins V R, Fensel D. Knowledge Engineering, Principles and Methods. Data and Knowledge Engineering,1998,25(122):161-197 [J]
[4]Neches R, Fikes R E, Gruber T R, etal. Enabling Technology for Knowledge Sharing. AIMagazine,1991,12(3):36～56 [J]
[5]Daniel Jurafsky & James H.Martin. Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Publishing House of Electronics Industry,2005 [J]
[6]Gruber T R. A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition,1993,5:199-220 [J]
[7]Guarino N. Semantic Matching:Formal Ontological Distinctions for Information Organization, Extraction,and Integration. In:Pazienza M T, eds. Information Extraction:A Multidisciplinary Approach to an Emerging Information Technology,Springer Verlag,1997,139～170 [J]
[8]Uschold M. Building Ontologies:Towards A Unified Methodology. In expert systems 96,1996Wordnet. http://www. cogsci. princeton. Edu, Framenet. http://www. icsi. berkeley. edu
[9]Gruber T R. Towards Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal of Hu2man2Computer Studies,1995,43:907-928 [J]
[10]Guarino N,Welty C. A Formal Ontology of Properties. In:Dieg R, Corby 0, eds. the Proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2000), Springer Verlag,2000,97～112 [J]
[11]Guarino N,Masolo C,Vetere G. OntoSeek:Content2Based Access to the Web. IEEE Intelligent Systems,1999,14(3):70～80 [J]
[12]Perez A G,Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods. Workshop on Ontologies and Problem2Solving Methods:Lessons Learned and Future Trends (IJCAI99), deAgosto, Estocolmo,1999 [Perez,1999] Perez A G,Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods. In:Stockholm V R,Benjamins B, Chandrasekaran A,eds. Proceedings of the IJCAI299 workshop on Ontologies and Problem2Solving Methods (KRR5) 1999,1～15 [J]
[13]Shun S B, Motta E, Domingue J. ScholOnto:an Ontology2based Digital Library Server for Research Documents and Discourse. Intl J Digital Libraries,2000,3 (3):237～ 248
[14]Bhatl CD. Mangement strategies for individual knowledge and organizational knowledge[J]. JKnowledgeManagement,2002,6(1):31-39
[15]Chandra S. Amaravadi, The Dimensions of Process Knowledge,Knowledge and Process Management,2005;12(1)
[16]Injun Choi, Jisoo Jung, Minseok Song. A Framework for the Integration of Knowledge Management and Business Process Management. Int. J. Innovationand Leaning,2004; (4 [J])
[17]付相君,李善平,郭鸣,产品数据模型的本体知识表达,计算机辅助设计与图形学学报,Mar,2005, vol.17 NO.3 [J]
[18]裴道武,关于模糊逻辑与模糊推理逻辑基础问题的十年研究综述,工程数学学报,[J]
[19]顾金睿,王芳关于本体论的研究综述情报科学June,2007, Vol,25, No.6 [J]
[20]夏锦文基于XML的本体知识建模语言九江师专学报(自然科学版)June,2003, No.6[I]
[21]傅泽田,郭永洪,敖丽敏,基于本体知识的诊断推理集成模型农业系统科学与综合研
究,May,2004,Vol.20,No.2[J]
[22]赵波,夏幼明,王泳,解继丽,基于对象的本体知识库设计云南师范大学学报,Apr.2004Vol.2 No.2[J]
[23]刘晓慧,佟伟光,林树宽,基于模糊推理的专家系统的研究与实现,Apr.2007Vol.3 NO.2 [J]
[24]柯旭贵,张佑生,基于实例推理的冲压模结构设计的框架知识表示,计算机工程与应用,2002.13[J]
[25]谈理,刘谨,梅丽婷,连续生产线设备故障诊断专家系统的动态模糊推理机制的研究,2005年6月Vol.7 N0.6[J]
[26]袁磊,面向领域知识的本体知识模型XML表示框架,计算机工程,January 2006
[27]丁卫平,顾卫江,董建成,祁恒,模糊逻辑推理在电子病历智能辅助诊断系统中的应用研究,南通大学学报(自然科学版)Dem.2006, Vol.5 No.4
[28]赵小芳,张勇,一种动态模糊逻辑程序设计语言[J],计算机工程,2007 Vol.32 No.6
[29]钱剑飞,何钦铭,陈华,俞璃争,一种基于模糊推理的细匹配方法,计算机工程,April 2007Vol.33 No.8[J]
[30]基于本体的中医舌诊知识的获取[J],曹宇峰,曹存根,计算机应用研究,2005.1
[31]王睿,杜静,何玉林,杨显刚,一种模糊知识库系统及其推理机制研究[J],计算机技术与发展,Mar.2007 Vol.17 No.3
[32]王海涛,曹存根,高颖,基于领域本体的半结构化文本知识自动获取方法的设计和实现[J],计算机学报vol.28,2005
[33]石磊,沈超,语义Web：服务描述框架研究综述[J],计算机技术与发展2006
[34]陈再旺,陈景长,一个医疗辅助诊断专家系统的设计与实现[J],计算机系统应用,2001,12
[35]林媛,陈新,崔智.面向对象的医疗诊断推理机设计[J].计算机应用与软件,2001,18(1)：5-9,42.
[36]赵卫东,盛昭瀚.基于形象思维的医疗诊断系统研究[J].系统工程理论与实践,2000,20(10)：108-113,
[37]张红梅,王永成.一个仿人疾病诊断专家系统模型[J].计算机应用研究,2000,17(1)：41-43.
[38]樊永正.模仿思维的医学专家系统[J].计算机研究与发展,1995,32(4)：62-65.
[39]张立群,李杰.一种应用于多专家会诊系统的调度专家算法[J].计算机应用研究,2000,17(3)：12-14.
[40]梁嘉骅,王双惠,李常洪.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,14(1)：83-90.
[41]花蕾.基于知识的肺癌早期细胞诊断系统[J].计算机应用研究,2000,17(2)：90-92.
[42]范逢曦,张海,卢轶郎等.急性心肌梗塞急性期预后专家系统的研究[J].中国生物医学工程学报,1992,11(1)：9-16.
[43]刘自伟.常见内科疾病中医诊疗专家辅助系统的设计及其实现[J].计算机时代,1994,(1)：1-6.
[44]Hudson,医学专家系统中的模糊逻辑[J].国外医学生物医学工程分册,1995,18(3)：148-154.
[45]徐宁,王宽全,张大鹏.基于神经网络的掌纹诊病专家系统[J].计算机应用研究,2001,18(2)：4-6.
[46]林东,邵军力.医学诊疗领域通用专家系统设计与实现[J].自动化学报,1995,21(3)：380-382.
[47]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报,2000,30(3)：46-50.
[47]梁嘉骅,王双惠,等.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,(1)：83-89
[48]龙硕柱,马光志,赵杰.医疗智能诊断系统的实现[J].计算机辅助工程,2003,(2)：75-79
[49]孙佰清,潘启树,等.医疗诊断系统专家知识的表达与获取方法[J].哈尔滨工业大学学报,2001,(1)：134-136
[50]邵虹,崔文成,等.医疗诊断专家系统研究进展[J].小型微型计算机系统,2003,(3)：509-512
[51]王海涛,曹存根,高颖.基于领域本体的半结构化文本知识自动获取方法的设计和实现[J].计算机学报,2005(12)：2010-2018
[52]龙硕柱,马光志,赵杰,医疗智能诊断系统的实现[J],计算机辅助工程,2003.2
[53]侯秀萍,袁秀丽,姜卓等,模糊逻辑技术在医学诊断中的应用研究[J],2005.3
[54]邵虹,崔文成,张继武等,医疗诊断专家系统研究进展[J],小型微型计算机系统,2003Vol.24 No.3
[55]赵卫东,盛昭瀚,杜雪寒,基于神经网络的案例推理医疗诊断[J],东南大学学报,2000Vol.30 No.3
[56]钱宗才,屈景辉,刘敬华等,专家系统知识表示及其在医疗诊断系统中的应用[J],医疗卫生装备,2003.10
[57]柳彦平,王文杰,荣江,基于RDF的医疗诊断专家系统[J],应用奇葩,2005.5
[58]陈铮,医疗诊断专家系统MDESY的设计与实现,微处理机,1996.3
[59]陈真诚,蒋勇,胥明玉等,人工智能技术及其在医学诊断中的应用及发展[J],生物医学工程学杂志,2002 Vol.19 No 3
[60]孙佰清,潘启树,冯英浚等,医疗诊断系统专家知识的表达与获取方法[J],哈尔滨工业大学学报,2000 Vol.33 No.1
[61]黄河清,神经网络辅助医学诊断系统的设计与实现[J],漳州职业大学学报,2002.3
[62]吴燕萍,闫强,计算机辅助医学诊断的理论模型及推理方法[J],2000 Vol.31 No.4
[63]陈再旺,陈景长,一个医疗辅助诊断专家系统的设计与实现[J],2001.12
[64]高黎,卜淮原,胡曙,一种医疗智能诊断推理机的设计与实现[J],2005.1
[65]侯秀萍,袁秀丽,姜早等,不确定性推理技术在医学诊断中的应用研究[J],计算机工程与应用,2005.14
[66]张勇,赵振杰,张德新,数据挖掘及其在医学中的应用[J],西北医学教育,2005 Vol.13 No.3
[67]陆汝钤,金芝,陈刚.面向本体的需求分析[J],软件学报,2000,11(8)：1009～1017.
[68]陈刚,陆汝钤,金芝.基于领域知识重用的虚拟领域本体构造[J],软件科学,2003；(3)
[69]宋红,林家瑞,医学诊断专家系统进展[J],国外医学生物医学工程分册,1995 Vol.18No.3
[70]姚一波,王纪亮,医疗诊断系统专家知识的表达与获取方法[J],信息技术,2002.2
[71]邱银安,林小红,一种病理诊断知识获取RS混合算法[J],科技通报,2005 Vol.21 No.3
[72]章沛,基于数据挖掘技术的青少年健康心理状况研究[J],科教兴市,2005.2
[73]金芝,基于本体的自动需求获取[J],计算机学报,2000,23(5)：486～492.
[74]杨叔子,郑晓军,人工智能与诊断专家系统[J],西安：西安交通大学出版社,1990.
[75]尹朝庆,尹皓,人工智能与专家系统[J],北京,中国水利出版社,2001
[76]俞思伟,医学专家系统的设计原理与实现方法[J],医学信息,2002,15(6).346～349
[77]张惠康,钱宗才,屈景辉等,专家系统及其在医疗诊断中的应用[J],第四军医大学学报,2002.23
[78]李锋刚,倪志伟,郜峦,案例推理技术在医学诊断专家系统中的设计思路探讨[J],中医药临床杂志,2005 Vol.17 No.2
[79]梁嘉骅,王双惠,李常洪等,医疗诊断专家系统开发的新思想与新方法[J],系统工程学报,1999 Vol.14 No.1
[1]Daniel Jurafsky & James H.Martin. Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition[J]. Publishing House of Electronics Industry,2005
[2]Peh Li Shiuan, Christoppher Ting Hian Ann. A Divide-Conquer Strategy for Parsing[J]. In:Proceedings of the ACL/SIGPARSF 5th Inernational Workshop on Parsing Technologies,1996
[3]Abney S. Parsing by chunks[J]. In:Berwick R, Abney S, Tenny C et al. Principle-BasedParsing. Dordrecht:Kluwer Academic Publishers,1991
[4]Steven Abney. Parsing by chunks[J]. In:Robert Berwick, Steven Abney, Carol Tenny. Principle-Based Parsing. Dordrecht. Kluwer Acahemic Publishers,1991
[5]Michael Bieberl, Ricki Goldman-Segall, Towards Knowledge-Sharing and Learning in Virtual Professional Communities[J], System Sciences,2002.35
[6]Noushin Ashrafi, Peng Xu, Jean-Pierre Kuilboer, Boosting Enterprise Agility via IT Knowledge Management Capabilities[J], System Sciences,2006.39
[7]Susan Gasson, Boundary-Spanning Knowledge-Sharing In E-Collaboration[J], System Sciences,2005.38
[8]Daniel D. Suthers, Collaborative Knowledge Construction through Shared Representations[J], System Sciences,2005.38
[9]Yannis Labrou, Tim Finin and Yun Peng, The Interoperability Problem:Bringing together Mobile Agents and Agent Communication Languages[J], System Sciences,1999.32
[10]Peter F. Patel-Schneider, The Semantic Web and Knowledge Representation[J], Computer Science,2004.5
[11]CHAU-YOUNG IVAN LIN, and CHENG-SEEN HO, An Ontology-Based Approach to Acquiring [J], Domain Knowledge for Requirement Analysis, Vol.24, No.1,2000.
[12]Fons Wi jnhoven, Edwin van den Belt, Eddy Verbruggen, Internal Data Market Services: An Ontology-Based Architecture and Its Evaluation[J], Informing Science,2003.7
[13]Geore F. Luger, Artificial Intelligence:Structures and Strategies for Complex Problem Solving, Pearson Education Limited,2002
[14]Minsky, M., A Framework for representing knowledge[J]. In Brachman and Levesque(1985),1975
[15]GENG Weidong, PAN Ynube, Fractal measure theory for knowledge representation[J], 1996 Vol.39 No.4
[16]CAO Cungen, Liveness characterization for FFC systems[J], SCIENCE IN CHINA Vol. 39 No.2,1996
[17]林媛,陈新,崔智.面向对象的医疗诊断推理机设计[J].计算机应用与软件,2001, 18(1)：5-9,42.
[18]樊永正.模仿思维的医学专家系统[J].计算机研究与发展,1995,32(4)：62-65.
[19]张红梅,王永成.一个仿人疾病诊断专家系统模型[J].计算机应用研究,2000,17(1)：41-43.
[20]赵卫东,盛昭瀚.基于形象思维的医疗诊断系统研究[J].系统工程理论与实践,2000,20(10)：108-113.
[21]张立群,李杰.一种应用于多专家会诊系统的调度专家算法[J].计算机应用研究,2000,17(3)：12-14.
[22]Hudson,医学专家系统中的模糊逻辑[J].国外医学生物医学工程分册,1995,18(3)：148-154.
[23]花蕾.基于知识的肺癌早期细胞诊断系统[J].计算机应用研究,2000,17(2)：90-92.
[24]范逢曦,张海,卢轶郎等.急性心肌梗塞急性期预后专家系统的研究[J].中国生物医学工程学报,1992,11(1)：9-16.
[25]徐宁,王宽全,张大鹏.基于神经网络的掌纹诊病专家系统[J].计算机应用研究,2001,18(2)：4-6.
[26]刘自伟.常见内科疾病中医诊疗专家辅助系统的设计及其实现[J].计算机时代,1994,(1)：1-6.
[27]林东,邵军力.医学诊疗领域通用专家系统设计与实现[J].自动化学报,1995,21(3)：380-382.
[28]梁嘉骅,王双惠,李常洪.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,14(1)：83-90.
[29]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报,2000,30(3)：46-50.
[30]梁嘉骅,王双惠,等.医疗诊断专家系统开发的新思想与新方法[J].系统工程学报,1999,(1)：83-89
[31]龙硕柱,马光志,赵杰.医疗智能诊断系统的实现[J].计算机辅助工程,2003,(2)：75-79
[32]孙佰清,潘启树,等.医疗诊断系统专家知识的表达与获取方法[J].哈尔滨工业大学学报,2001,(1)：134-136
[33]邵虹,崔文成,等.医疗诊断专家系统研究进展[J].小型微型计算机系统,2003,(3)：509-512
[34]郭茂祖,孙华梅,黄梯云,专家系统中知识库组织与维护技术的研究高技术通讯[J],2002,02
[35]王海涛,曹存根,高颖.基于领域本体的半结构化文本知识自动获取方法的设计和实现[J].计算机学报,2005(12)：2010-2018
[36]邓志鸿,唐世渭等,Ontology研究综述[J],北京大学学报,2002,9
[37]郑家三吉增涛王玉珠刘云,羊病诊断专家系统知识获取、知识表示的研究[J],2002,9
[38]张德海NKI国家和地区地理知识的获取与分析[J],2001
[39]黄荣怀李茂国沙景荣知识工程学：一个新的重要研究领域[J],2002
[40]赵铁军,机器翻译原理[M],哈尔滨工业大学出版社,2000年
[41]夏幼明等,基于非单调推理的领域专家知识库的研究[J],计算机科学,Vol.28 No.9,2001
[42]曹存根,国家基础设施的意义[J],中国科学院院刊,Vol.4,2001
[43]陆汝钤,世纪之交的知识工程与知识科学[J],清华大学出版社,2001年
[44]皮连生,知识分类与目标导向教学[J],华东师范大学出版社,1998年
[45]王永庆,人工智能原理与方法[M],西安交通大学出版社,1998年
[46]M. W. Eysenck (1990), The Blackwell Dictionary of Cognitive Psychology
[47]郭强,知识与社会的互动机制：社会知识化进程,http://www.modernization.com. cn/ guoql2.htm
[48]西安交通大学诊断与控制研究所编,知识工程的应用——从数据到知识[J],2000年8月http://www. monitoring. com.cn/ckddforum/zsgc. htm
[49]徐振宁,黄凯歌等,Ontology建模方法研究[J],计算机科学,2002 Vol..29 No.1
[50]王丽丽,曹存根等,基于本体论的民族知识获取和分析[J],计算机科学：2003 Vo l.30No.5
[51]周肖彬,曹存根等,基于本体的医学知识获取[J],计算机科学,2003 Vol..30 No.10
[52]王礼春,魏祥云,基于规则框架知识表示的配船专家系统[J],决策与决策支持系统,1996 Vol..6 No.1
[53]娄臻亮,张永清,阮雪榆,工程设计KEE系统[J]：知识处理技术,机械科学技术,2001Vol..20 No.4
[54]李龙澍,程慧霞,一种面向对象规则框架知识表示的研究与实践[J],小型微型计算机系统,1997 Vol.18 No.9
[55]邓志鸿,唐世渭,张铭等,Ontology研究综述[J],北京大学学报,2002 Vol.38 No.5
[56]杨艳,李健中,高宏,数字图书馆系统中基于Ontology的用户偏好模型[J],软件学报2005 Vol.16,No.12
[57]吴强,刘宗田,强宇,基于本体的知识库推理研究[J],计算机应用研究,2005
[58]岳静,张自力,本体表示语言研究综述[J],计算机科学,2006 Vol.33 No.2
[59]张东民,廖文和,胡建等,基于本体的设计知识建模[J],华南理工大学学报,2005 Vol.33 No.5
[60]眭跃飞,高颖,曹存根,NKI中的本体、框架和逻辑理论[J],软件学报,2005 Vol.16No.12
[61]张维明,宋峻峰,面向语义Web的领域本体表示、推理与集成研究[J],计算机研究与发展,2006 Vol.43 No.1
[62]周文,刘宗田,陈慧琼,FCA与本体结合研究的综述[J],计算机科学,2006 Vol.33 No.2
[63]雷玉霞,曹宝香,王书西,基于Ontology的概念联通在查询系统中的应用研究[J],计算机科学,2005 Vol.32 No.9
[64]吴江,赵宗涛,基于本体的非结构化知识库系统研究[J],计算机科学,2005 Vol.32No.9
[65]李善平,尹奇华,胡玉杰等,本体论研究综述[J],计算机研究与发展,2004 Vol.41 No.7
[66]徐长江,张优云,顾红亮,基于分层结构的汽轮机故障远程诊断的研究[J],计算机应用,2004 Vol.24
[67]孙海涛,宋荣兴,企业管理信息系统建设的框架手探析[J],石家庄经济学院学报,2000Vol.23 No.4
[68]王浩宇,蔡瑞英,一种面向对象推理模型及其知识表示[J],南京工业大学学报,2002 Vol.24 No.3
[69]规则框架的一种实用简化计算方法[J],广州大学学报,2001 Vol. 15 No.8
[70]龚元明,王小娟,树结构与框架推理[J],指挥技术学院学报,2000 Vol.11 No.5
[71]刘海龙,马孝江,基于框架理论的三相异步电动机故障诊断专家系统[J],机械工程师,2002.5
[72]付炜,地理专家知识表示的框架网络模型研究[J],地理研究,2002 Vol.21 No.3
[73]董明,查建中,杜玉明等,用面向对象的框架语言实现离散事件仿真[J],天津大学学报,1997 Vol.30 No.1
[74]于中华,唐常杰,张天庆,自然语言句法结构的框架树表示方法[J],小型微型计算机系统,1999 Vol.20 No.8
[75]朱绍文,张大斌,吕少鹏等,商业MIS框架设计辅助专家系统的知识处理方法[J],计算机工程与设计,1999 Vol.20 No.4
[76]蔡智明,刘宗田,黄自强,Web上分布式领域组件的框架表示[J],微电子学与计算机,1999.4
[77]何昭,李传湘,崔巍,基于面向对象框架的软件开发方法[J],计算机工程,2002Vol..28 No.4
[78]梁怡,人工智能、空间分析与空间决策[J],地理学报,1997 Vol.52 Supplement
[79]李莉, 欧灵,时态框架的推理策略[J],西南师范大学学报,1998 Vol.23 No.6
[80]朱绍文,吕少鹏,张大斌等,商业MIS框架生成系统的知识表示研究[J],小型微型计算机系统,1999 Vol.20 No.12
[81]于中华,唐常杰,张天庆等,一种用于建立句法结构框架树的有效分析算法[J],小型微型计算机系统,1999 Vol.20 No.9
[82]黄素英,王周敬,面向对象框架在信息系统开发中的应用[J],合肥工业大学学报,2003Vol.26 Supplement
[83]周晓庆,知识管理系统和CIMS系统的构建与设计[J],计算机应用,2005 Vol.25 No.9
[84]张志勤,李强,基于hibernate的知识库管理系统的开发[J],计算机与信息技术,2006
[85]张再跃,眭跃飞,曹存根,基于模糊命题模态逻辑的形式推理系统[J],软件学报,2005Vol.16 No.8
[86]钟诗胜,傅万涛,周济等,面向对象的模糊知识表达及推理[J],南昌大学学报,1995 Vol.19 No.3
[87]王拥军,倪平,王翠茹,缺省推理中的模糊知识[J],西安工业学院学报,2001 Vol.21No.1
[88]周大强,刘云光,加权模糊逻辑推理及在医学诊断专家系统中的应用[J],青海大学学报,1994 Vol.12 No.1
[89]凌云,基于多种知识表示下的常规推理机的设计与实现[J],微计算机应用,1996 Vol.17 No.3
[90]程晓春,刘叙华,基于模糊逻辑的不确定知识处理[J],计算机学报,1996 Vol.19 No.12
[91]曹存根,国家知识基础设施的意义[J],中国科学学院院刊,2001.4
[92]冯东辉,曹存根,知识界面在NKI中的应用[J],计算机工程与应用,2002.17
[93]秦前付,曹存根,安鹏等,基于装备水平和知识的空中作战效果分析[J],火力与指挥控制,2006.1
[94]秦前付,徐恍,曹存根,作战计划策略的表示与应用[J],系统工程理论与实践,2005.6
[95]沈一栋,知识工程.北京：科学技术出版社,1992
[96]唐素勤,刘立浩,曹存根,智能教学系统NKI-Tutor的教学策略研究[J],广西师范大学学报,2003 Vol.21 No.2
[97]曹存根,从专家分析实例中学习知识[J],软件学报,1994 Vol.5 No.6
[98]季秋,王万森,马建红,人工智能科学中的概率逻辑[J],计算机应用与软件,2006,Vol.23 No.1
[100]周涛,Apriori算法优化策略[J],福建电脑2006年第10期。
[101]娄兰芳,潘庆先,关联规则挖掘的一个高效预处理算法[J],烟台大学学报(自然科学与工程版)2007年1月
[102]曾舸,刘先锋关联规则挖掘中Apriori改进算法的研究[J],2007年第1期计算机与现代化
[103]孙志强,基于FP—Growth的入侵检测研究[J],计算机与通信工程学院,计算机技术与发展2006年12月
[104]张世海,李建国,宋健民,李超,基于改进Apriori算法的高层结构智能选型知识发现[J],2006年第6期总第91期建筑管理现代化
[105]吴沛,粟湘,基于关联规则挖掘的科技论文引文分析—以化学领域科技期刊为例[J],情报学报ISSN1000—0135第25卷第6期643—650
[106].张博,张虹,基于关系数据库的关联规则的形式化开采[J],第27卷 V0l.27第24期N0.24计算机工程与设计
[107]吴佳英,李平,郑金华,李少年基于兴趣度的时态关联规则挖掘算法[J],2006年第6期
[108]江彬,张学习,数据挖掘技术在现代化图书馆中的应用[J],情报探索2007年1月
[109]庞洁,率睿仙,胡建华,数据挖掘在电信交叉销售领域的研究[J],云南省昆明理工大学学报2006年第8期
[110]周翠红,贺建军,挖掘关联规则中对Apriori算法的一个改进[J],第15卷第4期2006年12月湖南城市学院学报(自然科学版)
[111]朱孝宇,王理冬,汪光阳,一种改进的Apriori挖掘关联规则算法[J],第2006年06期计算机技术与发展
[112]张永,迟忠先,一种高效的基于采样的关联规则挖掘算[J],计算机工程与应用,第11卷第4期2006年12月
[113]李环宇,杜春玲,李保银,一种基于关联规则挖掘的改进算法[J],2007年第1期 福建电脑
[114]朱可,胡克瑾,一种基于模糊关联规则挖掘的攻击识别系统[J],计算机科学2006Vol.33No.12(增刊)
[115]李陶深, 李新仕,一种新的基于投影的频繁模式树构造算法[J],计算机科学2006Vol.33No.12(增刊)
[116]赵海丰,邢永康,杨华丽,秦鹏,一种用于挖掘正、负关联规则的改进Apriori算法[J],计算机科学 2006Vol.33No_.12(增刊)
[117]黄勤,贺向前,刘益良,姚雪梅,遗传算法和关联规则算法用于入侵检测系统的研究[J],情报探索2007年1月
[118]任志波,遗传优化模糊约束的频繁项集挖掘[J],第26卷第10期2006年1O月北京理 工大学学报
[1]Borst W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis [J], University of Twente, Enschede,1997
[2]Kai Mertins, Peter Heisig, Jens Vorbeck et.al. Knowledge Management Concepts and Best Practices [J].Springer-Verleg Berlin Heidelbeg New York,2003
[3]Studer R, Benjamins VR, Fensel D. Knowledge Engineering, Principles and Methods [J].Data and Knowledge Engineering,1998,25 (122):161～197
[4]Neches R, Fikes RE, Gruber TR, et al. Enabling Technology for Knowledge Sharing [J]. AIMagazine,1991,12 (3):36～56
[5]Daniel Jurafsky & James H. Martin. Speech and Language Processing:An Introduction to Natural Language Processing [J], Computational Linguistics, and Speech Recognition. Publishing House of Electronics Industry,2005
[6]Gruber T R. A Translation Approach to Portable Ontology Specifications [J]. Knowledge Acquisition,1993,5:199～220
[7]Guarino N. Semantic Matching:Formal Ontological Distinctions for Information Organization, Extraction, and Integration [J]. In:Pazienza MT, eds. Information Extraction:A Multidisciplinary Approach to an Emerging Information Technology, Springer Verlag,1997,139～170
[8]Uschold M. Building Ontologies:Towards A Unified Methodology. In expert systems 96,1996 Wordnet. Http://www. Cogsci. Princeton. Edu Framenet. Http://www. Icsi. Berkeley. Edu
[9]Gruber T R. Towards Principles for the Design of Ontologies Used for Knowledge Sharing [J]. International Journal of Hu2man2Computer Studies,1995, 43:907-928
[10]Guarino N, Welty C. A Formal Ontology of Properties. In:Dieg R, Corby O, eds. The Proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management (EKAW'2000), [J], Springer Verlag,2000,97～112
[11]Guarino N, Masolo C, Vetere G. OntoSeek:Content2Based Access to the Web [J]. IEEE Intelligent Systems,1999,14 (3):70～80
[12]Perez AG, Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods.Workshop on Ontologies and Problem2Solving Methods:Lessons Learned and Future Trends (IJCAI99), deAgosto, Estocolmo,1999 [Perez,1999] Perez AG, Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem2SolvingMethods. In: Stockholm VR, Benjamins B, Chandrasekaran A, eds. Proceedings of the IJCAI299 workshop on Ontologies and Problem2Solving Methods (KRR5) 1999,1～15
[13]Shun SB, Motta E, Domingue J. ScholOnto:an Ontology2based Digital Library Server for Research Documents and Discourse [J]. Intl J Digital Libraries,2000,3 (3): 237-248
[14]Bhatl CD.Mangement strategies for individual knowledge and organizational knowledge [J]. JKnowledgeManagement,2002,6 (1):31-39
[15]Chandra S. Amaravadi, The Dimensions of Process Knowledge [J], Knowledge and Process Management,2005; 12 (1)
[16]Injun Choi, Jisoo Jung, Minseok Song. A Framework for the Integration of Knowledge Management and Business Process Management [J]. Int. J. Innovationand Leaning,2004; (4)
[17]of the pay-jun, Shan-Ping Li, Guo Mingfang, product data model of the body of knowledge [J], computer-aided design and graphics Journal, Mar,2005, vol.17 NO.3
[18]Wu Pei Road, on fuzzy logic and fuzzy logic based reasoning of the Decade Review [J], the Journal of Mathematics,
[19]Gu Jin Rui, Wang Fang on the ontology of the Review of Information Science June,2007, Vol,25, No.6 [J]
[20]Xia Jin-Wen XML-based body of knowledge modeling language Jiujiang College Journal (Natural Science) June,2003, No.6 [I]
[21]Fu Zetian, Guo Yonghong, Ao Limin, the body of knowledge-based diagnostic reasoning integrated model of agricultural systems science and comprehensive study [J], May,2004, Vol.20, No.2
[22]Zhao Bo, David Xia Ming, Wang Yong, Ji-li solution, based on the object of the body design [J], knowledge base of Yunnan Normal University Journal, Apr.2004Vol.2 No.2
[23]Liu Xiaohui, Tong Weiguang, Shu Lin Kuan, based on fuzzy reasoning Expert System Research and Implementation [J], Apr.2007Vol.3 NO.2
[24]Ke Xu Xiang, Zhang Yousheng, case-based reasoning of the structural design of stamping die in the framework of knowledge [J], computer engineering and application,2002.13
[25]on grounds that Liu would like, Li Mei-ting, continuous production line equipment fault diagnosis expert system for dynamic mechanism of fuzzy inference [J], in June 2005 Vol.7 NO.6
[26]Lei Yuan, and the domain knowledge of the XML body of knowledge model that framework [J], computer engineering, January 2006
[27]Ding Weiping, Gu Wei Jiang, Mr Tung Chee-chen, QI Heng, fuzzy logic in support of intelligent electronic medical records system of research [J], Nantong University Journal (Natural Science) Dem.2006, Vol.5 No.4
[28]Zhao Xiaofang, Zhang Yong, a dynamic fuzzy logic programming language computer engineering [J],2007 Vol.32 No.6
[29]money Jianfei, He Qin-ming, Chen Hua, Yu Li dispute over a fine based on fuzzy matching method of reasoning [J], computer engineering, April 2007 Vol.33 No.8 [J]
[30]Cao Yufeng, Cao stubs,based on the Chinese tongue inspection body of knowledge acquisition [J],, computer application research,2005.1
[31]Wang Rui, Du Jing and Yu-Lin He, Yang was just, a fuzzy knowledge base system and its mechanism of reasoning [J], computer technology and development, Mar.2007 Vol.17 No.3
[32]Wang Haitao, Cao stubs, Gao Ying, Based on the field of semi-structured body of the text automatically acquire knowledge of the design and implementation methods [J], computer Journal vol.28,2005
[33]Shi Lei, Shen Chao, Semantic Web:Review of the framework described services [J], computer technology and development,2006
[34]a medical diagnosis expert system design and implementation, re-Wang Chen, Chen Jing Long, computer applications,2001,12
[35]Lin Yuan, Chen, Cui. Object-oriented medical diagnostic reasoning machine design [J]. Computer application and software,2001,18 (1):5-9,42.
[36]Zhao, Sheng Zhaohan. Image of thinking based on the medical diagnosis system [J]. Systems engineering theory and practice,2000,20 (10):108-113.
[37]Zhang Hongmei, Wang Yongcheng. A humanoid disease diagnosis expert system model [J]. Computer Application Research,2000,17 (1):41-43.
[38]FAN Yong-being. Imitate thinking of the medical expert system [J]. Computer Research and Development,1995,32 (4):62-65.
[39]Zhang Liqun, Li Jie. A more expert diagnosis system for the dispatching of experts algorithm [J]. Computer Application Research,2000,17 (3):12-14.
[40]LIANG Jia-hua, Wang double-hui, Lee Hung. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal, 1999,14 (1):83-90.
[41]bud. Knowledge-based cell lung cancer early diagnosis system [J]. Computer Application Research,2000,17 (2):90-92.
[42]on Fan Xi, Zhang Hai, Lu Yi, such as Lang. Acute phase of acute myocardial infarction prognosis expert system of [J]. Chinese Journal of Biomedical Engineering, 1992,11 (1):9-16.
[43]Liu Wei. Diseases common medical clinics of Chinese medicine experts support the design and realization of [J]. The computer age,1994, (1):1-6.
[44]Hudson, medical experts in the fuzzy logic system [J]. Abroad medical biomedical engineering division,1995,18 (3):148-154.
[45]Xu Ning, Wang Kuan-wide, Da-Peng Chang. Palmprint based on neural network Diagnosis Expert System [J]. Computer Application Research,2001,18 (2):4-6.
[46]Dong, Shao Jun force. Generic field of medical clinics Expert System Design and Implementation [J]. Automation Journal,1995,21 (3):380-382.
[47]Zhao, Zhao-Han Sheng, DU Xue Han. Based on neural network of case-based reasoning medical diagnosis [J]. Journal of Southeast University,2000,30 (3):46-50.
[47]LIANG Jia-hua, Wang Hui-double, and so on. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal, 1999, (1):83-89
[48]Long-seok-chu, Ma Guang Zhi, Zhao Jie. Medical diagnosis system of [J]. Computer-aided engineering,2003, (2):75-79
[49]Sun Bai Qing, PAN Qi Shu, etc. medical diagnostic systems expert knowledge of expression and access methods [J]. Harbin Institute of Technology Journal,2001, (1):134-136
[50]Shaohong, Cui Wencheng, etc. medical diagnostic expert system Progress [J]. Small micro-computer systems,2003, (3):509-512
[51]Wang Haitao, Cao stubs, high-ying. Ontology based on the field of semi-structured text automatically acquire knowledge of methods of design and implementation of [J]. Computer Journal,2005 (12):2010-2018
[52]Long-seok-chu, Ma Guang Zhi, Zhao Jie, medical diagnosis system for smart [J], computer-aided engineering,2003.2
[53]Hou Xiuping, Yuan beautiful, Jiang Zhuo, fuzzy logic technology in medical diagnosis of applied research [J],2005.3
[54]Shaohong, Cui Wencheng, Ji-Wu Zhang, medical diagnostic expert system research progress [J], small micro-computer systems,2003 Vol.24 No.3
[55]Zhao, Zhao-Han Sheng, DU Xue Han, based on neural network of case-based reasoning medical diagnosis [J], the Journal of Southeast University,2000 Vol.30 No.3
[56]Qujing Hui, Liu Jinghua,money in it, expert system that knowledge in the medical diagnostic system and its application in medical and health equipment [J], 2003.10
[57]Liu Yanping, Wang Wenjie, Rongjiang, based on the RDF medical diagnosis expert systems [J], applications wonderful work,2005.5
[58]Chen Zheng, medical diagnostic expert system MDESY the design and implementation [J], the microprocessor,1996.3
[59]Chen sincere, Jiang Yong, Xu Ming-yu, and its artificial intelligence technology in medical diagnosis of and development [J] of biomedical engineering magazine, 2002 Vol.19 No 3
[60]Sun Bai Qing, PAN Qi Shu, Ying Feng Zhijun, medical diagnostic systems expert knowledge of expression and access methods, Harbin Institute of Technology Journal,2000 Vol.33 No.l
[61]-the Yellow River, neural network-assisted medical diagnosis system design and implementation [J], Zhangzhou Vocational University Journal,2002.3
[62]Wu Yanping, Yan-keung, computer-aided medical diagnosis of the theoretical models and reasoning [J],2000 Vol.31 No.4
[63]re-Wang Chen, Chen Jing Long, a medical diagnosis expert system design and implementation [J] 2001.12
[64]High Lebanon, the BU Huai, Hu Shu, a medical diagnostic reasoning Intelligent Design and Implementation of the machine [J],2005.1
[65]Hou Xiuping, Yuan beautiful, as early as ginger, uncertainty reasoning technology in medical diagnosis of applied research, engineering and computer applications [J],2005.14
[66]Zhang Yong, Zhao Zhenjie, Zhang Dexin, data mining and its Application in Medicine [J], Northwest Medical Education,2005 Vol.13 No.3
[67]Mr Luk Qian, Jin-zhi, Chen Gang. Bulk of the demand-oriented analysis [J]. Journal of Software,2000,11 (8):1009 to 1017.
[68]Chen Gang, Mr Luk Qian, Jin-zhi. Based on the field of knowledge reuse areas of the virtual body structure [J]. Software science,2003; (3)
[69]Song Hong, Lin Jia-rui, the medical diagnosis expert system progress, and foreign medical bio-medical engineering division [J],1995 Vol.18 No.3
[70]wave of Yao, Wang Ji-liang, medical diagnostic imaging expert knowledge of expression and access methods [J], information technology,2002.2
[71]Chiu Yin-an, LIN Xiao-hong, a pathological diagnosis RS mixed method of knowledge acquisition [J], technology communications,2005 Vol.21 No.3
[72]Zhang Pei, based on data mining techniques of psychological status of adolescent health research [J], the city through science and education,2005.2
[73]Jin-zhi, for the bulk of the demand for automatic access [J]. Computer Journal, 2000,23 (5):486-492.
[74]Yang Shuzi, ZHENG Xiao-jun, artificial intelligence and diagnostic expert system [J], Xi'an:Xi'an Jiaotong University Press,1990.
[75]Yin Chao-Ching, Yin Hao, artificial intelligence and expert systems [J], Beijing, China Water Conservancy Press,2001
[76]Yu Siwei, the medical expert system design principles and implementation methods [J], medical information,2002,15 (6).346-349
[77]Zhang Huikang, the money in it, Qujing Hui, expert systems in medical diagnosis and the application of the Fourth Military Medical University Journal [J], 2002.23
[78]Li Fenggang, Ni Zhiwei, Gao Luan, case-based reasoning technology in medical diagnosis expert system in the design of the study [J], in the Clinical Journal of Medicine,2005 Vol.17 No.2
[79]LIANG Jia-hua, Wang double-hui, Li Hung Chang, medical diagnostic expert system developed by new ideas and new methods [J], systems engineering Journal, 1999 Vol.14 No.1
[1]Daniel Jurafsky & James H.Martin. Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Publishing House of Electronics Industry,2005
[2]Peh Li Shiuan, Christoppher Ting Hian Ann. A Divide-Conquer Strategy for Parsing. In: Proceedings of the ACL/SIGPARSF 5th Inernational Workshop on Parsing Technologies,1996
[3]Abney S. Parsing by chunks. In:Berwick R, Abney S, Tenny Cetal. Principle-BasedParsing. Dordrecht:Kluwer Academic Publishers,1991
[4]Steven Abney. Parsing by chunks. In:Robert Berwick, Steven Abney, Carol Tenny. Principle-Based Parsing. Dordrecht. Kluwer Acahemic Publishers,1991
[5]Michael Bieberl, Ricki Goldman-Segalll, Towards Knowledge-Sharing and Learning in Virtual Professional Communities, System Sciences,2002.35
[6]Noushin Ashrafi, Peng Xu, Jean-Pierre Kuilboer, Boosting Enterprise Agility via IT Knowledge Management Capabilities, System Sciences,2006.39
[7]Susan Gasson, Boundary-Spanning Knowledge-Sharing In E-Collaboration, System Sciences,2005.38
[8]Daniel D. Suthers, Collaborative Knowledge Construction through Shared Representations, System Sciences,2005.38
[9]Yannis Labrou, Tim Finin and Yun Peng, The Interoperability Prob]em:Bringing together Mobile Agents and Agent Communication Languages, System Sciences,1999.32
[10]Peter F. Patel-Schneider, The Semantic Web and Knowledge Representation, Computer Science,2004.5
[11]CHAU-YOUNG IVAN LIN, and CHENG-SEEN HO, An Ontology-Based Approach to Acquiring Domain Knowledge for Requirement Analysis, Vol.24, No.1,2000.
[12]Fons Wijnhoven, Elwin van den Belt, Eddy Verbruggen, Internal Data Market Services: An Ontology-Based Architecture and Its Evaluation, Informing Science, 2003.7
[13]Geore F. Luger, Artificial Intelligence:Structures and Strategies for Complex Problem Solving, Pearson Education Limited,2002
[14]Minsky, M., A Franework for reprosenting knowledge. In Brachman and Levesque(1985),1975
[15]GENG Weidong, PAN Ynube, Fractal measure theory for knowledge representation,1996 Vol.39 No.4
[16]CAO Cungen, Liveness characterization for FFC systems, SCIENCE IN CHINA Vol.39 No.2,1996
17] Lin Yuan, Chen, Cui. Object-oriented medical diagnostic reasoning machine design [J]. Computer application and software,2001, 18 (1):5-9,42.
[18]FAN Yong-being. Imitate thinking of the medical expert system [J]. Computer Research and Development,1995,32 (4):62-65.
[19]Zhang Hongmei, Wang Yongcheng. A humanoid disease diagnosis expert system model [J]. Computer Application Research.2000,17 (1):41-43.
[20]Zhao, Sheng Zhaohan. Image of thinking based on the medical diagnosis system research [J]. Systems engineering theory and practice,2000,20 (10):108-113.
[21]Zhang Liqun, Li Jie. A more expert diagnosis system for the dispatching of experts algorithm [J]. Computer Application Research, 2000,17 (3):12-14.
[22]Hudson, medical experts in the fuzzy logic system [J]. Abroad medical biomedical engineering division,1995,18 (3):148-154.
[23]bud. Knowledge-based cell lung cancer early diagnosis system [J]. Computer Application Research,2000,17 (2):90-92.
[24]on Fan Xi, Zhang Hai, Lu Yi, such as Lang. Acute phase of acute myocardial infarction prognosis expert system of [J]. Chinese Journal of Biomedical Engineering,1992,11 (1):9-16.
[25]Xu Ning, Wang Kuan-wide, Da-Peng Chang, Palmprint based on neural network Diagnosis Expert System [J]. Computer Application Research,2001, 18 (2):4-6.
[26]Liu Wei. Diseases common medical clinics of Chinese medicine experts support the design and realization of [J]. The computer age,1994, (1): 1-6.
[27]Dong, Shao Jun force. Generic field of medical clinics Expert System Design and Implementation [J]. Automation Journal,1995,21 (3):380-382.
[28]LIANG Jia-hua, Wang double-hui, Lee Hung. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal,1999,14 (1):83-90.
[29]Zhao, Zhao-Han Sheng, DU Xue Han. Based on neural network of case-based reasoning medical diagnosis [J]. Journal of Southeast University,2000,30 (3):46-50.
[30]LIANG Jia-hua, Wang Hui-double, and so on. Medical diagnosis expert system development of new ideas and new methods of [J]. Systems Engineering Journal,1999, (1):83-89
[31]Long-seok-chu, Ma Guang Zhi, Zhao Jie, Medical diagnosis system of [J]. Computer-aided engineering,2003, (2):75-79
[32]Sun Bai Qing, PAN Qi Shu, etc. medical diagnostic systems expert knowledge of expression and access methods [J]. Harbin Institute of Technology Journal,2001, (1):134-136
[33]Shaohong, Cui Wencheng, etc. medical diagnostic expert system Progress [J]. Small micro-computer systems,2003, (3):509-512
[34]Guomao Zu, Sun Hua Mei, HUANG Ti-yun, expert systems in the organization and maintenance of technical knowledge base of high-tech communications,2002,02
[35]Wang Haitao, Cao stubs, Gao Ying. Ontology based on the field of semi-structured text automatically acquire knowledge of methods of design and implementation of [J]. Computer Journal,2005 (12):2010-2018
[36]Teng, Tang Wei, and so on, Ontology Review, the Journal of Peking University,2002,9
[37]Andrew Kyrgyzstan by three-Tao Wang Yuzhu Liu, sheep disease diagnosis expert system access to knowledge, knowledge that the study, 2002,9
[38]ZHANG De-hai NKI countries and regions geographical knowledge acquisition and analysis,2001
[39]Huang Ronghuai Mao-Guo Li Sha King-knowledge engineering:a new and important field of research,2002
[40]Zhao Tiejun, machine translation principle [M], Harbin Institute of Technology Press,2000
[41]David Ming Xia, nonmonotonic reasoning based on the knowledge base experts in the field of research, computer science, Vol.28 No.9,2001
[42]Cao stubs, the significance of national infrastructure, the Chinese Academy of Sciences Institute published in, Vol.4,2001
[43]Mr Luk Qian, the turn of the century of knowledge and knowledge of science and engineering, Tsinghua University Press,2001
[44]Paper Lian-sheng, knowledge classification and goal-oriented teaching, East China Normal University Press,1998
[45]Wang Yung-ching, artificial intelligence principles and methods [M], Xi'an Jiaotong University Press,1998
[46]MW Eysenck (1990), The Blackwell Dictionary of Cognitive Psychology
[47]Guo Qiang, knowledge and social interaction mechanism:the process of knowledge-based society, http://www.modernization.com.cn/ guoq12.htm
[48]Xi'an Jiaotong University Institute for diagnosis and control, the application of engineering knowledge-from data to knowledge, in August 2000 http://www.monitoring.com.cn/ckddforum/zsgc.htm
[49]Xu Zhenning, songs of praise, such as yellow, Ontology Modeling studies, computer science,2002 Vol..29 No.1
[50]Wang Lili, Cao stubs and so on, based on the ontology of the national knowledge acquisition and analysis, computer science:2003 Vo 1.30No. 5
[51]Zhou Xiao Bin, Cao stubs and so on, based on the body of medical knowledge acquisition, Computer Science,2003 Vol..30 No.10
[52]Wang Lichun, Wei Xiangyun, rule-based framework with the knowledge that the ship expert system, decision-making and decision support system, 1996 Vol..6 No.1
[53]Zhen-Liang Lou, Zhang Yongqing, Ruan Xueyu, engineering design KEE system:knowledge processing technology, mechanical science and technology,2001 Vol..20 No.4
[54]Lee Yong Shu, Cheng Huixia, an object-oriented framework of rules that knowledge of research and practice, small micro-computer systems, 1997 Vol.18 No.9
[55]Teng, Tang Wei, Zhang Ming, and so on, Ontology Review, the Journal of Peking University,2002 Vol.38 No.5
[56]Yang Yan, Li Jian, Gao Hong, the digital library system based on user preferences Ontology model, Journal of Software 2005 Vol.16, No.12
[57]Wu Qiang, LIU Zong-tian, strong-yu, the knowledge base reasoning Ontology-based research, computer application research,2005
[58]Jing Yue, Zhang Zili, body language said Review, Computer Science, 2006 Vol.33 No.2
[59]Zhang Dong Min, Liao Wen-he, Jian Hu, based on the design of the' Ontology knowledge modeling,South China University of Technology Journal,2005 Vol.33 No.5
[60]Sui Yue Fei, Gao Ying, Cao stubs, NKI in the body, the theoretical framework and logic, software Journal,2005 Vol.16 No.12
[61]Zhang Weiming, Song Junfeng, and the Semantic Web domain ontology said, reasoning and integration research, computer research and development,2006 Vol.43 No.1
[62]Zhou, Zong-Tian Liu, Chen Huiqiong, FCA and the bu.k of the studies reviewed, computer science,2006 Vol.33 No.2
[63]Lei Yuxia, Cao Bao-xiang, Wang West, based on the concept of Unicom Ontology in the query system of applied research, comput er science,2005 Vol.32 No.9
[64]Wu Jiang, Zong-Tao Zhao, the ontology Based unstru:tured knowledge base system, computer science,2005 Vol.32 No.9
[65]Shan-Ping Li, Yinqi China, Hu Yujie, ontology Revi sw, the Computer Research and Development,2004 Vol.41 No.7
[66]Xu Chang jiang, Zhang Youyun, Ou Hongliang, based on the hierarchical structure of the turbine failure of the long-range re earch, computer application,2004 Vol.24
[67]Hai-Tao Sun, Song Rongxing. enterprise managerent information system construction in the frame (?)ork of hand, Shijia huang Institute of Economic Journal,2000 Vol.2(?) No.4
[68]Wang Haoyu, Cai Ruiying, a model and its object-oriented reasoning knowledge that the Nanjing University of Technology Journal,2002 Vol. 24 No.3
[69]a practical framework of rules to simplify the calculation method, the Journal of Guangzhou University,2001 Vol.15 No.8
[70]Gong Yuan-ming, Wang Xiaojuan, structure and framework of the tree reasoning, the command Institute of Technology Journal,2000 Vol.11 No.5
[71]Liu Hailong, MA Xiao Jiang, based on the theoretical framework of the three-phase asynchronous motor fault diagnosis expert system, mechanical engineers,2002.5
[72]Fu Wei, geography expert knowledge that the framework network model, geographic research,2002 Vol.21 No.3
[73]Dong, Zha Jianzhong, Yu-Ming Du, using object-oriented language in the framework of discrete event simulation, Journal of Tianjin University,1997 Vol.30 No.1
[74]in China, Tang Chang Jie, Zhang Qing-day, natural language syntax structure of the framework of the tree that way, small micro-computer systems,1999 Vol.20 No.8
[75]Zhu Shaowen, Zhang Da-bin, Lvshao Peng, commercial MIS framework design expert system for supporting knowledge processing methods, computer engineering and design,1999 Vol.20 No.4
[76]Cai Zhiming, Zong-Tian Liu, Huang Ziqiang, Web Distributed on the field components of the framework, micro-electronics and computers, 1999.4
[77]He Zhao, Li Xiang, Cui Wei, based on object-oriented framework for software development methods, computer engineering,2002 Vol..28 No.4
[78]Liang Yi, artificial intelligence, spatial analysis and decision-making space, geographical Journal,1997 Vol.52 Supplement
[79]Li Li, Europe Ling, temporal reasoning framework of the strategy, Journal of the Southwest Normal University,1998 Vol.23 No.6
[80]Zhu Shaowen, Lvshao Peng, Zhang Da-bin, commercial MIS framework generation system of knowledge that study, small micro-computer systems, 1999 Vol.20 No.12
[81]in China, Tang Chang Jie, Zhang Qing, and other days, the syntactic structure of the establishment of a framework for the effective analysis of tree algorithm, small micro-computer systems,1999 Vol.20 No.9
[82]Huang Su-ying, Wang Zhou Jing, object-oriented framework for the development of information systems applications, Hefei University of Technology Journal,2003 Vol.26 Supplement
[83]Zhou Xiaoqing, knowledge management systems and CIMS system Construction and design, computer application,2005 Vol.25 No.9
[84]Zhi-Qin Zhang, Li Qiang, based on hibernate the knowledge base management system development, computer and information technology,2006
[85]re-yue Zhang, Sui Yue Fei, Cao stubs, based on fuzzy proposition in the form of modal logic reasoning systems, software Journal,2005 Vol.16 No.8
[86]Chung Shi-sheng, Fu million Tao, Zhou Ji, the fuzzy object-oriented knowledge and their reasoning, Journal of Nanchang University,1995 Vol. 19 No.3
[87]Wang Yongjun, Ni Ping, Wang Ru, default reasoning in the fuzzy knowledge, Xi'an Institute of Industrial Journal,2001 Vol.21 No.1
[88]weeks of strong, Liu Yun-kwong, the weighted fuzzy logic and expert systems in medical diagnosis of, Qinghai University Journal,1994 Vol. 12 No.1
[89]Lingyun, a variety of knowledge based on the conventional reasoning that the design and implementation of micro-computer applications,1996 Vol.17 No.3
[90]Xiao-Chun Cheng and Liu, Syria, based on fuzzy logic of uncertainty knowledge processing, computer Journal,1996 Vol.19 No.12
[91]Cao stubs, the state of knowledge of the significance of infrastructure, the Chinese scientific Xueyuanyuankan,2001.4
[92]Ping Donghui, Cao stubs, knowledge interface NKI in the application of engineering and computer applications,2002.17
[93]before the Qin pay stubs Cao, An Peng, equipment and knowledge-based aerial combat effectiveness analysis, firepower and command and control, 2006.1
[94]to pay before the Qin and Xu Huang, Cao stubs, operational planning strategy and that the application of systems engineering theory and practice,2005.6
[95]-1, knowledge engineering. Beijing:Science and Technology Press, 1992
[96]Tang Su-qin, Liuli Hao, Cao stubs, intelligent tutoring system NKI-Tutor of the Study, Journal of Guangxi Normal University,2003 Vol. 21 No.2
[97]Cao stubs from the expert analysis of examples to learn knowledge, software Journal,1994 Vol.5 No.6
[98]JIQIU, Wang Sen, Ma Jian-hong, the probability of artificial intelligence in the logic of science, computer application and software, 2006, Vol.23 No.1
[100]Zhou Tao, Apriori algorithm optimization strategy, Fujian in 2006 the first computer 10.
[101]Lou Lanfang, the first Qing Pan, the Mining Association Rules Pretreatment a highly efficient algorithms, Yantai University Journal (Natural Sciences and Engineering) in January 2007
[102]had Ge, LIU Xian-feng mining association rules in the Algorithm Apriori improve the first study in 2007 with a modern computer
[103]Sun Zhiqiang, based on the FP-Growth of the intrusion detection research, the Computer and Communications Engineering Institute, computer technology and development in December 2006
[104]Zhang Hai, Li Jianguo, Song Min, Li Chao, based on improved algorithms Apriori intelligent selection of high-level knowledge that in 2006 the first section 6 of 91 phase construction management modernization
[105]Wu Pei, Su-hsiang, the mining association rules based on the scientific papers citation analysis-to the field of chemical science and technology periodicals as an example, the intelligence Journal ISSN1000-0135 Volume 25 No.6 643-650
[106]. Zhang Bo, Zhang Hong, the relational database based on the formal mining association rules Vol.27 VO1.27 No.24 NO.24 computer engineering and design
[107]Wu Jiaying, Li Ping, Jin-Hua Zheng, Li juvenile based on the degree of interest when the state mining association rules algorithm,2006 No. 6
[108]Jiang-bin, Zhang learning, data mining technology in the modern library in the application of intelligence to explore in January 2007
[109]Pang Jie, the rate Rui cents, Hu Jianhua, data mining in the telecommunications research in the field of cross-selling, Polytechnic University Journal of Kunming in Yunnan Province in 2006 the first eight
[11]Zhou Cuihong, He Jianjun, the rules of the Mining Association of Apriori an improved algorithm, Vol 15 No.4 December 2006 Journal of Hunan City College (Natural Science)
[111]Zhu Xiaoyu, Wang Dong, Yang Guang, an improved algorithm Apriori Mining Association Rules,2006 06 computer technology and development
[112]Chang, Chi-chung first, based on an efficient sampling of the association rule mining operators, computer engineering a id application of article 11 paragraph 4 volumes in December 2006
[113]Lee Huan, the Du Chunling, Li Yin, a mining association rules based on the improved algorithm,2007 No.1 computer Fujian
[114]Zhu, Hu Ke Jin, a mining association rules based on fuzzy attack identification systems, computer science 2006 Vol.33No.12 (supplement)
[115]Lee Tao-shen, Li Shi, a new projection based on the frequent pattern tree structure algorithms, computer science 2006 Vol.33No.12 (supplement)
[116]Zhao Haifeng, XING Yongjian Kang, Yang gorgeo(?)s, Peng Qin, excavation for a positive and negative association rules to improve Apriori algori(?)hm, computer science 2006 Vol.33No.12 (supplement)
[117]Qin Huang, HE forward, Liu Liang, Yaoxue Mei, genetic algorithms and algorithms for the association rules of intrusion detection systems, intelligence exploration in January 2007
[118]Ren Zhi-bo, genetic optimization of fuzzy constraints of the frequent mining, Vol 26 No.10 October 2006 Beijing Institute of Technology Journal