多专业领域本体的构建及语义检索研究
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摘要
本体在软件工程、人工智能、信息检索、Web服务发现等领域中扮演着越来越重要的角色。依照领域依赖程度,可以将本体分为通用本体(Top Ontology)、领域本体(Domain Ontology)、任务本体(Task Ontology)和应用本体(ApplicationOntology)。领域本体可以有效地组织领域中的知识,使知识更好地共享、重用。有一些领域,如农业领域、铁路领域、高速铁路领域、航空领域等,这些领域都由不同的专业领域构成,如高速铁路领域由工务工程、牵引供电、动车组、运营管理等专业领域构成。本文面向由多专业构成的领域,以铁道部科技研究开发计划重大课题(Z2006-094)和教育部科技基础资源数据平台项目(507002)为支撑,以高速铁路领域为实证背景,研究多专业领域本体构建方法论和基于多专业领域本体的应用——语义检索和推理,主要做了以下几个方面的工作:
(1)提出了一种多专业领域本体构建方法
本文面向由多专业构成的领域,在对已有本体构建方法和由多专业构成的领域知识特点深入研究的基础上,探讨了多专业领域本体构建思路,提出了一种建立在“范畴表和主题词表”基础上的多专业领域本体构建方法MMDOB(MultipleMaiors Domain Ontology Building)。此方法先基于叙词表或范畴与主题词表构建各专业领域本体,然后集成为统一的多专业领域本体。在由各专业领域本体集成为多专业领域本体时,根据由多专业构成的领域的实际情况,提出一种多专业领域本体集成方法MMDOI(Multiple Majors Domain Ontology Integration)和一个三层的多专业领域本体集成框架。
(2)给出了多专业领域本体模型和概念语义相似度计算方法
针对集成后的统一的多专业领域本体,在对现有的本体模型和多专业领域本体深入研究的基础上,提出了多专业领域本体模型的八元组表示方法和概念的九元组表示方法及其形式化描述方式。
概念语义相似度计算是语义扩展检索中用户查询词扩展必不可少的一种方法,基于本文给出的多专业领域本体概念模型,构建了计算多专业领域本体概念之间语义相似度的MD4(Fourfold Matching-Distance Model)模型,并给出了该模型的详细算法。
(3)给出了五种语义扩展检索模式
扩展检索是通过查找检索条件中的相关概念,从而得到相关知识项。本文针对多专业领域本体的特点和其检索对象对用户查询语义扩展模式进行了分析,给出了五种语义扩展检索模式。以OWL为推理基础,对领域本体语义关系和推理规则进行了阐述。
(4)给出了在本文方法指导下的多专业领域——高速铁路领域的实证分析
在本文提出的多专业领域本体构建方法MMDOB的指导下,构建了高速铁路领域各专业本体,并将其集成为统一的多专业领域本体;在此基础上,本文设计和开发了一个面向高速铁路知识的语义检索和推理系统HSRK-SRRS(High-speedRailway Knowledge-Semantic Retrieval and Reasoning System),对本文提出的基于“范畴表和主题词表”的多专业领域本体构建方法、语义扩展检索方法和语义关系推理进行了实验验证。
本文提出的多专业领域本体构建方法有一定的现实意义,对多专业领域本体构建、概念之间语义关系分析和多专业领域本体集成有一定的借鉴作用;同时,基于多专业领域本体模型构建的计算领域本体概念之间语义相似度的MD4模型,为由多专业构成的领域本体概念之间的语义关系提供了一种有效的量化方法;本文将多专业领域研究成果应用于高速铁路领域,为由多专业构成的领域本体的构建及其语义检索与推理的实现提供了一个实证参考。
Ontology is playing an increasingly important role in the fields of software engineering, artificial intelligence, information retrieval and web service research. According to the level of dependence on research fields ontology can be divided into Top Ontology, Domain Ontology, Task Ontology and Application Ontology. Domain ontology can effectively organize the knowledge of that domain and make it easier to share and reuse. Some kinds of domains such as agriculture, railway, high-speed railway and aviation include different major fields. For example, the high-speed railway domain consists of maintenance engineering, traction power supply, EMU and operation management etc. major fields. Oriented to the domain which consists of several major fields, this dissertation researches construction methodology for multiple majors domain ontology and semantic retrieval and reasoning based on multiple majors domain ontology. This dissertation makes following contributions:
(1) Proposes MMDOB (Multiple Majors Domain Ontology Building) method
Based on the construction methods of domain ontology and the feature of multiplemajors domain knowledge, and oriented to the domain which consists of several major fields, this dissertation puts forward MMDOB method based on thesaurus and thematic words. First, MMDOB method builds ontologies of the major fields based on thesaurus and thematic words, and then integrates these ontologies into a unified ontology for the multiple major domain. According to actual situation of the domain which consists of several major fields, this dissertation proposes MMDOI (Multiple Majors Domain Ontology Integration) method and three layers ontology integration framework for multiple majors domain.
(2) Gives a model for multiple majors domain ontology and the method for concept semantic similarity computation
Based on the ontology model and multiple majors domain ontology, this dissertation proposes Eight-tuples model for multiple majors domain ontology, Nine-tuples model for the concept of multiple majors domain ontology and formalized description for multiple majors domain ontology.
Concept semantic similarity computation is an essential method of semantic extension retrieval. On the basis of concept model for multiple majors domain ontology, this dissertation builds MD4 (Fourfold Matching-Distance Model) to compute concept semantic similarity and it also provides the computation detail.
(3) Gives five kinds of semantic expansion models
Extended retrieval is through searching the related concepts in search terms to find the related knowledge. According to the feature of multiple majors domain ontology and the search object, this dissertation analyses semantic expansion model of user query and proposes five models for semantic expansion retrieval. This dissertation chooses description logic that is the logic foundation of OWL as the reasoning foundation.
(4) Gives an empirical study for High-speed railway domain
Under the guidance of MMDOB method, this dissertation builds various majors ontology of high-speed railway, and they are preliminary merged into one unified domain ontology of high-speed railway. On this basis, this dissertation designs and develops HSRK-SRRS (High-speed Railway Knowledge-Semantic Retrieval and Reasoning System). It tests MMDOB method, semantic extension retrieval and reasoning model.
MMDOB method that is proposed in this dissertation has some practical significance, and it also has a certain reference for ontology construction of multiple majors domain, semantic relations analysis between concepts and ontology integration of multiple majors domain. This dissertation builds MD4 based on the model for multiple majors domain ontology to compute domain ontology Concept Semantic Similarity. The experiment to test MD4 shows that the model can reflect the semantic relation between concepts. So it gives an effective method to quantify the semantic relation between concepts in domain ontology that are constituted with several major fields. These research results are applied in high-speed railway domain, and that provides a positive reference for the acchivement of ontology construction and semantic retrieval and reasoning for the domain which consists of several major fields.
(1)提出了一种多专业领域本体构建方法
本文面向由多专业构成的领域,在对已有本体构建方法和由多专业构成的领域知识特点深入研究的基础上,探讨了多专业领域本体构建思路,提出了一种建立在“范畴表和主题词表”基础上的多专业领域本体构建方法MMDOB(MultipleMaiors Domain Ontology Building)。此方法先基于叙词表或范畴与主题词表构建各专业领域本体,然后集成为统一的多专业领域本体。在由各专业领域本体集成为多专业领域本体时,根据由多专业构成的领域的实际情况,提出一种多专业领域本体集成方法MMDOI(Multiple Majors Domain Ontology Integration)和一个三层的多专业领域本体集成框架。
(2)给出了多专业领域本体模型和概念语义相似度计算方法
针对集成后的统一的多专业领域本体,在对现有的本体模型和多专业领域本体深入研究的基础上,提出了多专业领域本体模型的八元组表示方法和概念的九元组表示方法及其形式化描述方式。
概念语义相似度计算是语义扩展检索中用户查询词扩展必不可少的一种方法,基于本文给出的多专业领域本体概念模型,构建了计算多专业领域本体概念之间语义相似度的MD4(Fourfold Matching-Distance Model)模型,并给出了该模型的详细算法。
(3)给出了五种语义扩展检索模式
扩展检索是通过查找检索条件中的相关概念,从而得到相关知识项。本文针对多专业领域本体的特点和其检索对象对用户查询语义扩展模式进行了分析,给出了五种语义扩展检索模式。以OWL为推理基础,对领域本体语义关系和推理规则进行了阐述。
(4)给出了在本文方法指导下的多专业领域——高速铁路领域的实证分析
在本文提出的多专业领域本体构建方法MMDOB的指导下,构建了高速铁路领域各专业本体,并将其集成为统一的多专业领域本体;在此基础上,本文设计和开发了一个面向高速铁路知识的语义检索和推理系统HSRK-SRRS(High-speedRailway Knowledge-Semantic Retrieval and Reasoning System),对本文提出的基于“范畴表和主题词表”的多专业领域本体构建方法、语义扩展检索方法和语义关系推理进行了实验验证。
本文提出的多专业领域本体构建方法有一定的现实意义,对多专业领域本体构建、概念之间语义关系分析和多专业领域本体集成有一定的借鉴作用;同时,基于多专业领域本体模型构建的计算领域本体概念之间语义相似度的MD4模型,为由多专业构成的领域本体概念之间的语义关系提供了一种有效的量化方法;本文将多专业领域研究成果应用于高速铁路领域,为由多专业构成的领域本体的构建及其语义检索与推理的实现提供了一个实证参考。
Ontology is playing an increasingly important role in the fields of software engineering, artificial intelligence, information retrieval and web service research. According to the level of dependence on research fields ontology can be divided into Top Ontology, Domain Ontology, Task Ontology and Application Ontology. Domain ontology can effectively organize the knowledge of that domain and make it easier to share and reuse. Some kinds of domains such as agriculture, railway, high-speed railway and aviation include different major fields. For example, the high-speed railway domain consists of maintenance engineering, traction power supply, EMU and operation management etc. major fields. Oriented to the domain which consists of several major fields, this dissertation researches construction methodology for multiple majors domain ontology and semantic retrieval and reasoning based on multiple majors domain ontology. This dissertation makes following contributions:
(1) Proposes MMDOB (Multiple Majors Domain Ontology Building) method
Based on the construction methods of domain ontology and the feature of multiplemajors domain knowledge, and oriented to the domain which consists of several major fields, this dissertation puts forward MMDOB method based on thesaurus and thematic words. First, MMDOB method builds ontologies of the major fields based on thesaurus and thematic words, and then integrates these ontologies into a unified ontology for the multiple major domain. According to actual situation of the domain which consists of several major fields, this dissertation proposes MMDOI (Multiple Majors Domain Ontology Integration) method and three layers ontology integration framework for multiple majors domain.
(2) Gives a model for multiple majors domain ontology and the method for concept semantic similarity computation
Based on the ontology model and multiple majors domain ontology, this dissertation proposes Eight-tuples model for multiple majors domain ontology, Nine-tuples model for the concept of multiple majors domain ontology and formalized description for multiple majors domain ontology.
Concept semantic similarity computation is an essential method of semantic extension retrieval. On the basis of concept model for multiple majors domain ontology, this dissertation builds MD4 (Fourfold Matching-Distance Model) to compute concept semantic similarity and it also provides the computation detail.
(3) Gives five kinds of semantic expansion models
Extended retrieval is through searching the related concepts in search terms to find the related knowledge. According to the feature of multiple majors domain ontology and the search object, this dissertation analyses semantic expansion model of user query and proposes five models for semantic expansion retrieval. This dissertation chooses description logic that is the logic foundation of OWL as the reasoning foundation.
(4) Gives an empirical study for High-speed railway domain
Under the guidance of MMDOB method, this dissertation builds various majors ontology of high-speed railway, and they are preliminary merged into one unified domain ontology of high-speed railway. On this basis, this dissertation designs and develops HSRK-SRRS (High-speed Railway Knowledge-Semantic Retrieval and Reasoning System). It tests MMDOB method, semantic extension retrieval and reasoning model.
MMDOB method that is proposed in this dissertation has some practical significance, and it also has a certain reference for ontology construction of multiple majors domain, semantic relations analysis between concepts and ontology integration of multiple majors domain. This dissertation builds MD4 based on the model for multiple majors domain ontology to compute domain ontology Concept Semantic Similarity. The experiment to test MD4 shows that the model can reflect the semantic relation between concepts. So it gives an effective method to quantify the semantic relation between concepts in domain ontology that are constituted with several major fields. These research results are applied in high-speed railway domain, and that provides a positive reference for the acchivement of ontology construction and semantic retrieval and reasoning for the domain which consists of several major fields.
引文
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