一种基于社会选择的本体聚类与合并机制
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摘要
语义web为网页扩展了计算机可理解的、可处理的语义信息,然而由于本体数量激增导致的异构本体现象阻碍了语义的通信与融合.本体合并是解决本体异构的有效途径之一,旨将多个由agent构建的异构源本体通过本体合并机制形成一个共享的顶层本体,以期形成一个更大的语义共享空间.本文将本体合并看作是社会选择的一种应用,用于分析个体源本体与决策共享本体之间的关系.由于构建者的背景知识和推理能力不同会对合并结果产生影响,因此本文综合考虑源本体的可信度和一致赞同属性,设计了包含本体聚类器和本体聚集器的本体合并机制.首先,以社会选择和描述逻辑为基础构建本体合并框架和具体流程;在此基础上设计了基于距离的本体聚类算法,以减少不可信本体对合并结果的不利影响;接着对社会选择中的聚集函数进行总结和改进,并将其应用在本体合并中,介绍了积分聚集规则和阶梯性聚集规则.最后,本文对本体聚集规则的一致赞同属性做出分析,并通过对比实验验证了本体合并机制的有效性.
The semantic web extends computer understandable and processing semantic information for web pages,however the phenomenon of heterogeneous ontology caused by the multiplication of ontologies in the same domain has hindered semantic communication and fusion. Ontology merging is an effective solution to ontology heterogeneity in semantic web which focuses on merging a group of individual local ontologies with distinct sources as a shared collective ontology to form a larger semantic shared space. We can view the ontology merging as the problem of social choice such as voting theory and judgement aggregation,analyzing the relationship between the individual local ontology and shared collective ontology. The collective ontology will be affected by diverse background knowledge and reasonable abilities that different ontology builders has. Therefore,in this paper we consider the reliability as well as unanimity,and propose the ontology merging mechanism that includes ontology clustering and ontology aggregation. We firstly describe the framework and steps of ontology merging based on description logic,and design the distance-based ontology clustering to reduce the negative impact of unreliable ontologies. Then we summarize and improve the aggregation functions,apply aggregation rules to ontology merging and present the scoring aggregation rule as well as staged-elimination rule. Finally,we study the unanimity of aggregation rules and verify the effectiveness of ontology merging mechanism by experimental comparison results.
The semantic web extends computer understandable and processing semantic information for web pages,however the phenomenon of heterogeneous ontology caused by the multiplication of ontologies in the same domain has hindered semantic communication and fusion. Ontology merging is an effective solution to ontology heterogeneity in semantic web which focuses on merging a group of individual local ontologies with distinct sources as a shared collective ontology to form a larger semantic shared space. We can view the ontology merging as the problem of social choice such as voting theory and judgement aggregation,analyzing the relationship between the individual local ontology and shared collective ontology. The collective ontology will be affected by diverse background knowledge and reasonable abilities that different ontology builders has. Therefore,in this paper we consider the reliability as well as unanimity,and propose the ontology merging mechanism that includes ontology clustering and ontology aggregation. We firstly describe the framework and steps of ontology merging based on description logic,and design the distance-based ontology clustering to reduce the negative impact of unreliable ontologies. Then we summarize and improve the aggregation functions,apply aggregation rules to ontology merging and present the scoring aggregation rule as well as staged-elimination rule. Finally,we study the unanimity of aggregation rules and verify the effectiveness of ontology merging mechanism by experimental comparison results.
引文
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[20]Artale A,Kontchakov R,Wolter F,et al.Temporal description logic for ontology-based data access[C]//Proceedings of International Joint Conference on Artificial Intelligence,AAAI Press,2013:711-717.
[21]Jiang Y,Wang X,Zheng H T.A semantic similarity measure based on information distance for ontology alignment[J].Information Sciences,2014,278(10):76-87.
[22]Gao W,Farahani M R,Hosamani S,et al.Distance learning techniques for ontology similarity measuring and ontology mapping[J].Cluster Computing,2017,20(2):959-968.
[23]Wu J,Liu H,Xiong H,et al.A theoretic framework of K-meansbased consensus clustering[C]//Proceedings of International Joint Conference on Artificial Intelligence,AAAI Press,2013:1799-1805.
[10]王顺,康达周,江东宇.本体映射综述[J].计算机科学,2017,44(9):1-10.
[12]唐杰,梁邦勇,李涓子,等.语义Web中的本体自动映射[J].计算机学报,2006,29(11):1956-1976.
[15]张楠,陈荣,郭世凯.投票理论研究现状及其展望[J].计算机科学,2015,42(5):1-9.
[2]Maedche,Alexander,Staab.Ontology learning for the semantic web[J].Intelligent Systems IEEE,2001,16(2):72-79.
[3]Fensel D,Bussler C.The web service modeling framework WSMF[J].Electronic Commerce Research&Applications,2003,1(2):113-137.
[4]Li G Y,Liu S P,Zhao-Yan.Formal concept analysis based ontology merging method[C]//Proceedings of International Conference on Computer Science and Information Technology,IEEE,2010:279-282.
[5]Brandt F,Conitzer V,Endriss U.Computational social choice[J].International Symposium on Theoretical Aspects of Computer Science,2012,7(29):68-72.
[6]Noy N F,Musen M A.PROMPT:algorithm and tool for automated ontology merging and alignment[C]//Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence,AAAI Press,2000:450-455.
[7]Stumme G,Madche A.FCA-merge:bottom-up merging of ontologies[C]//Proceedings of 7th International Conference on Artificial Intelligence,Seattle,WA,2001:225-230.
[8]Mcguinness D L.An environment for merging and testing large ontologies[C]//Proceedings of Conference on Principiles of Knowledge Representation and Reasoning,Morgan Kaufman Publishers,2000:483-493.
[9]Noy N F,Musen M A.SMART:automated support for ontology merging and alignment[C]//Proceedings of the Twelfth Banff Workshop on Knowledge Acquistion Modeling&Management Banff Algeberta,1999:1-24.
[10]Wang Shun,Kang Da-zhou,Jiang Dong-yu.Survey of ontology mapping[J].Computer Science,2017,44(9):1-10.
[11]Li G Y,Liu S P,Zhao-Yan.Formal concept analysis based ontology merging method[C]//Proceedings of International Conference on Computer Science and Information Technology,IEEE,2010,8(4):279-282.
[12]Tang Jie,Liang Bang-yong,Li Juan-zi,et al.Automatic ontology mapping in semantic web[J].Chinese Journal of Computers,2006,29(11):1956-1976.
[13]Porello D,Endriss U.Ontology merging as social choice:judgment aggregation under the open world assumption[J].Journal of Logic&Computation,2014,24(6):1229-1249.
[14]Porello D,Endriss U.Ontology merging as social choice[C]//Proceedings of International Conference on Computational Logic in Multi-Agent Systems,Springer,Verlag,2011:157-170.
[15]Zhang Nan,Chen Rong,Guo Shi-kai.State-of-art and future of voting theory[J].Computer Science,2015,42(5):1-9.
[16]Teng L,Zhu J,Li B,et al.A voting aggregation algorithm for optimal social satisfaction[J].Mobile Networks&Applications,2017,2017(99):1-8.
[17]Zouaq A,Gasevic D,Hatala M.Voting theory for concept detection[C]//Proceedings of International Conference on the Semantic Web:Research and Applications,Springer,Verlag,2012:315-329.
[18]Dietrich F.Scoring rules for judgment aggregation[J].Social Choice&Welfare,2014,42(4):873-911.
[19]Pigozzi G,Slavkovik M,Torre L V D.A complete conclusionbased procedure for judgment aggregation[C]//Proceedings of Algorithmic Decision Theory,First International Conference,Venice,Italy,2009:1-13.
[20]Artale A,Kontchakov R,Wolter F,et al.Temporal description logic for ontology-based data access[C]//Proceedings of International Joint Conference on Artificial Intelligence,AAAI Press,2013:711-717.
[21]Jiang Y,Wang X,Zheng H T.A semantic similarity measure based on information distance for ontology alignment[J].Information Sciences,2014,278(10):76-87.
[22]Gao W,Farahani M R,Hosamani S,et al.Distance learning techniques for ontology similarity measuring and ontology mapping[J].Cluster Computing,2017,20(2):959-968.
[23]Wu J,Liu H,Xiong H,et al.A theoretic framework of K-meansbased consensus clustering[C]//Proceedings of International Joint Conference on Artificial Intelligence,AAAI Press,2013:1799-1805.
[10]王顺,康达周,江东宇.本体映射综述[J].计算机科学,2017,44(9):1-10.
[12]唐杰,梁邦勇,李涓子,等.语义Web中的本体自动映射[J].计算机学报,2006,29(11):1956-1976.
[15]张楠,陈荣,郭世凯.投票理论研究现状及其展望[J].计算机科学,2015,42(5):1-9.