基于多文本知识源的历史知识修正与获取
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摘要
知识获取是知识工程中关键的一环,而从文本知识源中获取专业知识是一种重要而常用的途径。而不同文本知识源对同一对象描述往往有所不同,为了获取完备的高精度和细粒度的知识,给出了一种从多种文本知识源中获取历史知识的方法。其基本思想是:将不同文本知识源的知识自动翻译成框架知识;对描述相同对象的不同框架进行不一致性检测并修正;合并知识框架。实验表明,该方法能获取到高精度和细粒度的历史知识,是可行和有效的,并为下一步的知识服务奠定重要的基础。
Knowledge acquisition is the key step in knowledge engineering, and acquiring professional knowledge from texts is an important and commonly used way. However, different texts have different description on the same object. In order to acquire complete knowledge of high-precise and fine-grained, this paper provides a method for acquiring history knowledge from multiple texts knowledge sources, which can be described as follows: automatically translating history knowledge into frames; inconsistency detection and revision, and merging related frames. The experimental results show that this method is feasible and effective. It can acquire high-precise and fine-grained history knowledge, and lay important basis for knowledge service.
Knowledge acquisition is the key step in knowledge engineering, and acquiring professional knowledge from texts is an important and commonly used way. However, different texts have different description on the same object. In order to acquire complete knowledge of high-precise and fine-grained, this paper provides a method for acquiring history knowledge from multiple texts knowledge sources, which can be described as follows: automatically translating history knowledge into frames; inconsistency detection and revision, and merging related frames. The experimental results show that this method is feasible and effective. It can acquire high-precise and fine-grained history knowledge, and lay important basis for knowledge service.
引文
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[2]Colobran M,Basart J M.Knowledge based concept analysis method using concept maps and UML:security notion case[J].World Academy of Science,Engineering and Technology,2013,7:437-439.
[3]Santos E,Dinh H T.Consistency of test cases in validation of Bayesian knowledge-bases[J].Tool with Artificial Intelligence,2004:468-475.
[4]Cao C G,Lu R N.The knowledge processing of CONBES[J].Advances in Chinese Computer Science,1998,3:25-39.
[5]生佳根,刘思峰.基于本体的知识获取、管理和应用方法研究[D].南京:南京航空航天大学,2011.
[6]陈川波,蔺志青.基于半结构化文本信息抽取的简历识别系统[D].北京:北京邮电大学,2008.
[7]曹月芹.基于模糊知识粒度的混合属性约简算法[J].计算机工程与应用,2013,49(17):108-109.
[8]郝天永,曹存根.基于PADL的古代人物简介知识获取[J].计算机科学,2005,32(30):169-174.
[9]李娜.历史人物的知识修正与获取[J].数据库与信息管理,2008(4):1379-1381.
[10]罗贝,吴洁,曹存根,等.从文本中获取植物知识方法的研究[J].计算机科学,2005,30(10):35-39.
[11]李娜,朱连章.基于本体的文本知识修正与获取[D].东营:中国石油大学,2009.
[12]雷玉霞,眭跃飞,曹宝香.基于知识本体的属性分析以及概念联通[J].计算机科学,2004(3):102-104.
[13]张节松,肖庆宪.一种新的属性分类方法与应用刍议[J].计算机工程与应用,2014,50(2):15-20.
[14]Nguyen N T.Processing inconsistency of knowledge on semantic level[J].J UCS,2005,11(2):285-302.
[15]桑妍丽,梁吉业.基于粗糙信息向量的一种决策规则算法[J].计算机工程与应用,2009,45(17):136-138.
[16]不列颠百科全书[M].北京:中国大百科全书出版社,2002.
[17]世界知识大辞典[M].北京:世界知识出版社,1998.