基于OWL的领域本体构建方法研究
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摘要
本体作为一种能在语义和知识层次上描述知识系统的概念模型和建模工具,自被提出就引起了国内外众多科研人员的关注,并在计算机的许多领域得到了广泛应用。但是本体的构建研究还处于初步阶段,本体自动构建方法的研究已经成为本体研究的重点。本文利用自然语言处理技术通过对特定领域的知识资源的利用,提出了基于OWL(Ontolgoy Web Ontology)的领域本体构建方法,并针对本体应用时所需的关键技术-本体推理进行了相关研究,具体研究内容主要划分为几个方面:
(1)叙词表到本体的转化。利用已经存在的领域知识对自动构造本体大有裨益,充分利用叙词表的特点,通过对特定领域的叙词表资源进行重构利用,抽取出可以利用的概念术语,通过一系列规则方法来丰富和精炼概念间的语义关系,特别是针对本体语义关系准确性难题提出了解决方案。相关实验结果表明,构建的本体中等级关系和非等级关系的准确性分别达到了89.02%和83.44%,取得了令人满意的效果。
(2)提出了一种新的全自动的关系数据库到本体的转换方法。通过分析关系模式的主键、属性、引用关系、完整性约束来创建本体,给出了一组框架逻辑描述的关系模式到本体概念和层次的映射规则。相对于已有的转换方法,该方法在最大限度地保持关系模式的完整性的同时,转换和迁移过程无需人工参与,基于规则保证了转换过程的自动化。
(3)为了实现领域本体的构建,提出了一种基于OWL的本体集成方法,将分别由叙词表和数据库得到的本体进行合并,得到最终的领域本体。此方法在语言级和结构级对本体的分析更为细致,有一定的性能改进。
(4)设计一个适应本体语言特点的推理系统架构,以控制规则易于补充,本文提出了基于SWRL的本体推理技术,通过建立规则进行逻辑推理,以发现本体中潜在而未知的关系。
Ontolgoy, as a kind of model of concept and modeling tool to describe the knowledge system in semantic and knowlege level, has attracted the attention from many researchers at home and abroad, since it comes out. It has been widely applied in many areas of the computer. However, research on ontology construction is still in its initial stage, and the research of automatic construction has been the key of ontological study. In this paper, we propose a kind of method of automatic domain ontology construction based on OWL by using the domain knowledge resources with natural language processing technology, and make some related research on ontological reasoning, the key technology of ontological application. The specific content of research is divided into several major aspects:
(1) To use the existing domain knowledge on the ontology constructin is with the great benefit. Considering the characteristics of the domain thesauri fully, we reengineer the domain thesauri, extract the concepts that are applicable for ontology, and with a series of rules enrich and refine the semantic relatins of the concepts, especially propose the solution to the problem of the accuracy of semantic relations. The test result shows the accuracy of the hierachical relations and non-hieachcal relations separately is 89.02% and 83.44%, and that is satisfying.
(2) A new fully automated method to convert the relational database into the ontology is proposed. Ontology is constructed by analyzing the primary key, attributes, reference relations, and integrity constraints of the ralation schema, and we give a set of mapping rules from the relation schema to concepts and hiberarchy of ontology. Compared to the existing conversion methods, this method maintains the integrity of relation schema maximumly and also its conversion and migration processes proceed without artificial participation. This rule-based method ensures the automation of the conversion.
(3) A kind of method of ontology integration based on OWL is proposed, in order to achieve the construction of domain ontology. The ontologies from thesauri and database are merged, and the ulitmate ontolgoy is constructed. This method are more detailed at linguistic and structure levels, and the performance is improved at a certain level.
(4) To design the architecture of ontology reasoning system to adapt the characteristic of ontology language makes it easy to add the rules. This paper proposes the ontology reasoning technology based on SWRL, the logic reasoning is done by the rules to discover the potential and unknown ontological relationship.
(1)叙词表到本体的转化。利用已经存在的领域知识对自动构造本体大有裨益,充分利用叙词表的特点,通过对特定领域的叙词表资源进行重构利用,抽取出可以利用的概念术语,通过一系列规则方法来丰富和精炼概念间的语义关系,特别是针对本体语义关系准确性难题提出了解决方案。相关实验结果表明,构建的本体中等级关系和非等级关系的准确性分别达到了89.02%和83.44%,取得了令人满意的效果。
(2)提出了一种新的全自动的关系数据库到本体的转换方法。通过分析关系模式的主键、属性、引用关系、完整性约束来创建本体,给出了一组框架逻辑描述的关系模式到本体概念和层次的映射规则。相对于已有的转换方法,该方法在最大限度地保持关系模式的完整性的同时,转换和迁移过程无需人工参与,基于规则保证了转换过程的自动化。
(3)为了实现领域本体的构建,提出了一种基于OWL的本体集成方法,将分别由叙词表和数据库得到的本体进行合并,得到最终的领域本体。此方法在语言级和结构级对本体的分析更为细致,有一定的性能改进。
(4)设计一个适应本体语言特点的推理系统架构,以控制规则易于补充,本文提出了基于SWRL的本体推理技术,通过建立规则进行逻辑推理,以发现本体中潜在而未知的关系。
Ontolgoy, as a kind of model of concept and modeling tool to describe the knowledge system in semantic and knowlege level, has attracted the attention from many researchers at home and abroad, since it comes out. It has been widely applied in many areas of the computer. However, research on ontology construction is still in its initial stage, and the research of automatic construction has been the key of ontological study. In this paper, we propose a kind of method of automatic domain ontology construction based on OWL by using the domain knowledge resources with natural language processing technology, and make some related research on ontological reasoning, the key technology of ontological application. The specific content of research is divided into several major aspects:
(1) To use the existing domain knowledge on the ontology constructin is with the great benefit. Considering the characteristics of the domain thesauri fully, we reengineer the domain thesauri, extract the concepts that are applicable for ontology, and with a series of rules enrich and refine the semantic relatins of the concepts, especially propose the solution to the problem of the accuracy of semantic relations. The test result shows the accuracy of the hierachical relations and non-hieachcal relations separately is 89.02% and 83.44%, and that is satisfying.
(2) A new fully automated method to convert the relational database into the ontology is proposed. Ontology is constructed by analyzing the primary key, attributes, reference relations, and integrity constraints of the ralation schema, and we give a set of mapping rules from the relation schema to concepts and hiberarchy of ontology. Compared to the existing conversion methods, this method maintains the integrity of relation schema maximumly and also its conversion and migration processes proceed without artificial participation. This rule-based method ensures the automation of the conversion.
(3) A kind of method of ontology integration based on OWL is proposed, in order to achieve the construction of domain ontology. The ontologies from thesauri and database are merged, and the ulitmate ontolgoy is constructed. This method are more detailed at linguistic and structure levels, and the performance is improved at a certain level.
(4) To design the architecture of ontology reasoning system to adapt the characteristic of ontology language makes it easy to add the rules. This paper proposes the ontology reasoning technology based on SWRL, the logic reasoning is done by the rules to discover the potential and unknown ontological relationship.
引文
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2 Y. Ding, S. Foo. Ontology Research and Development: Part 1-a Review of Ontology Generation. Information Science. 2002, 28(2): 234~260
3杨秋芬,陈跃新. Ontology方法学综述.计算机与信息技术. 2001, (10): 2~6
4 A. Maedche, S. Staab. Discovering Conceptual Relations from Text. In, ECAI 2000. Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000: 158~164
5 A. Maedche, S. Staab. Mining Ontologies from Text. In: Dieng, R. & Corby, O. EKAW-2000-12th International Conference on Knowledge Engineering and Knowledge Management. 2000: 112~118
6 G. Neumann, R. Backofen, J. Baur, M. Becker and C. Braun. An Information Extraction Core System for Real World German Text Processing. In, ANL 97 Proceedings of the Conference on Applied Natural Language Processing, Washington, USA, 208~215
7 R. Srikant, R. Agrawal. Mining Generalized Association Rules. In Proc. Of VLDB 95, 1995, 407~419
8 C. H. Hwang. Incompletely and Imprecisely Speaking Using Dynamic Ontologies for Representing and Retrieving Information. In. Proceeding of the 6th International Workshop on Knowledge Representing meets Databases (KRDB 99), Linkoping, Sweden, July 29,1999, 201~207
9常春.联合国粮食与农业组织AOS项目.图书情报杂志学刊. 2003, (2): 44~45
10 FAO. http://www.fao.org/agis/aos
11王军. Vision:集成分类法、主题词表和语义元数据的概念网络.情报学报, 2003, 22(4): 412~418
12 R. Neches, R. E. Fikes, T. R. Gruber, et al. Enabling Technology for Knowledge Sharing. AI Magazine. 1991, 12(3): 36~56
13 T. R. Gruber. A Translation Approach to Portable Ontology Specifications.Knowledge Acquisition. 1993, (5): 199~220
14 W. N. Borst. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis, University of Twente, Enschede. 1997
15 R. Studer, V. R. Benjamins, D. Fensel. Knowledge Engineering, Principles and Methods. Data and Knowledge Engineering. 1998, 25(122): 161~197
16李善平,尹奇,胡玉杰.本体论研究综述.计算机研究与发展, 2004, 41(7): 1041~1052
17 M. K. Smith, C. Welty, L. Deborah. OWL Web Ontology Language Guide Recommendation [EB/OL]. [2004-02-10]. http://www.w3.org/TR/owl-guide/
18姜恩波. RDF原理、结构初探.现代图书情报技术. 2001,(5): 32~33
19陶皖,廖述梅.当前本体编辑工具的分析与研究.计算机工程与设计. 2005,(3): 122~124
20 Ontolingua. http://www.ksl.stanford.edu/software/ontolingua/
21 Protégé. http://protege.stanford.edu/
22杜文华,董慧.本体建设工具比较研究.情报杂志, 2005. 21(2): 7~9
23高茂庭,王正欧. Ontology及其应用.计算机应用, 2003(S2): 35~37
24汪鹏. Ontology知识表示的艺术.计算机教育, 2004, 3(7):45~47
25杜小勇,李曼,王珊.本体学习研究综述.软件学报, 2006, 6(9): 1837~1847
26 M. Uschold, M. Gruninger. Ontolgies: Pinciples, Methods, and Applications. Knowledge Engineering Review, 1996, 11(2): 93~155
27 M. Gruninger, M. S. Fox. The Design and Evaluation of Ontologies for Enterprise Engineering. In: Workshop on Implemented Ontolgies, European Workshop on Artificial Intelligence, Amsterdam, NL, 1994, 105~128
28 M. Fernandez, A. Gomez-perez, N. Juristo. METHONTOLOGY: From Ontological Art Towards Ontological Engineering, AAAI-97 Spring Symposium on Ontological Engineering, Stanford University, March 24-26th, 1997: 101~106
29 WordNet 3.0. Jan 16th 2007. http://wordnet.princeton.edu/perl/webwn
30 Cycl. http://www.cycl.org
31董振东,董强,郝长伶.知网的理论发现.中文信息学报. 2007, (4): 3~9
32詹卫东.面向自然语言处理的大规模语义知识库研究综述.中文信息处理若干问题.科学出版社. 2003: 107~201
33 S. D. Richardson, W. B. Dolan, L. Vanderwende. MindNet: Acquiring and Structuring Semantic Information from Text. In: Proceeding of the Joint ACLand Coling Conference Montreal. 1998: 1098~1102
34耿崇,耿骞. Ontology的自动构建方法.信息系统. 2006, 29(3): 341~344
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