基于二值相似度计算的异构本体融合方法
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摘要
异构本体的存在带来了知识检索的冗余,基于异构本体的知识融合是十分必要的。大量的语义相似度计算容量与复杂的计算过程使得知识融合变得困难,本文提出二值相似度计算的异构本体融合方法,将语义相似度的计算提前至原始本体的构建过程,融合时只进行概念和关系的二值匹配,从而简化融合过程再次计算语义相似度的过程。文章从实体图书元数据、小样本本体和大样本本体三个角度组织了三个实验,利用武汉大学图书馆书目数据的实验一显示本文方法可以完成本体融合的过程,实验二和实验三显示本文方法可以提高本体融合的准确性,并显著提高运行反馈时间,综合反映本体融合效果良好,但需要在召回率上进行改进。本文方法有望在扩展专家本体、减少本体构建开销等方面体现应用价值。
Heterogeneous ontology causes redundancy in knowledge retrieval. Therefore, knowledge fusion based on het-erogeneous ontology is necessary. However, because of the massive capacity and complicated processes required for se-mantic similarity computing, knowledge fusion has become less simple. In this paper, we propose an ontology fusion meth-od based on binary metrics of semantic similarity calculation. In the fusion process, there will be only binary matching, thus aiming to further simplify the calculation of fusion from semantic similarity. Thus, the present research represents a shift from methods locating computing progress at the beginning of original ontology construction. We adopted three experi-ments to test the usability of our approach, from the perspectives of(1) actual library resources,(2) a small dataset, and(3)a large dataset. In experiment one, bibliographic data from Wuhan University Library were used to test our proposals feasi-bility and capabilities. Results showed that our approach can completely merge two ontologies into a single theme. The sec-ond and third experiments both verified that our approach has the ability to accurately detect merging couples and decrease time cost. The tests demonstrated a good overall fusion result; nevertheless, recall requires future improvement. This meth-od is expected to extend the implementation of expert ontology and aid in cost reduction of ontology construction.
Heterogeneous ontology causes redundancy in knowledge retrieval. Therefore, knowledge fusion based on het-erogeneous ontology is necessary. However, because of the massive capacity and complicated processes required for se-mantic similarity computing, knowledge fusion has become less simple. In this paper, we propose an ontology fusion meth-od based on binary metrics of semantic similarity calculation. In the fusion process, there will be only binary matching, thus aiming to further simplify the calculation of fusion from semantic similarity. Thus, the present research represents a shift from methods locating computing progress at the beginning of original ontology construction. We adopted three experi-ments to test the usability of our approach, from the perspectives of(1) actual library resources,(2) a small dataset, and(3)a large dataset. In experiment one, bibliographic data from Wuhan University Library were used to test our proposals feasi-bility and capabilities. Results showed that our approach can completely merge two ontologies into a single theme. The sec-ond and third experiments both verified that our approach has the ability to accurately detect merging couples and decrease time cost. The tests demonstrated a good overall fusion result; nevertheless, recall requires future improvement. This meth-od is expected to extend the implementation of expert ontology and aid in cost reduction of ontology construction.
引文
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[6]Wu Z X,Tian X Y.Research of ontology merging based on con-cept similarity[C]//Proceedings of the Seventh International Con-ference on Measuring Technology and Mechatronics Automation.IEEE,2015:831-834.
[7]于晓繁,王效岳,白如江.本体集成方法和工具综述[J].现代图书情报技术,2011(1):14-21.
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[11]米杨,曹锦丹.基于PROMPT的本体映射实例分析[J].情报学报,2010,29(6):987-991.
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[14]do Amaral M B,Roberts A,Rector A L.NLP techniques associat-ed with the OpenGALEN ontology for semi-automatic textual ex-traction of medical knowledge:abstracting and mapping equiva-lent linguistic and logical constructs[J].Proceedings of AMIA An-nual Symposium,2000:76-80.
[15]徐健,方安,洪娜.一种基于词语相似度计算的本体映射方法[J].现代图书情报技术,2013(2):36-42.
[16]姚晓明,王锋,林兰芬,等.一种高效的多策略本体映射方法[J].中国科技论文,2013,8(7):642-647.
[17]李凯,李万龙,郑山红,等.改进的多策略本体映射方法[J].吉林大学学报(信息科学版),2016,34(4):536-542.
[18]裘江南,李丽冬,吴力文,等.基于传递的语义相关度计算方法研究[J].情报学报,2010,29(4):749-758.
[19]裘江南,李丽冬,吴力文,等.本体中同种语义关系间的可传递规律研究[J].情报学报,2009,28(5):658-663.
[20]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.
[21]于娟,熊振辉,欧忠辉.基于哈斯图的本体偏序关系消冗方法研究[J].情报学报,2015,34(3):279-285.
[22]Maree M,Belkhatir M.Addressing semantic heterogeneity through multiple knowledge base assisted merging of domainspecific ontologies[J].Knowledge-Based Systems,2015,73:199-211.
[23]郭强,关欣,潘丽娜,等.一种基于条件证据网络的多源异类知识融合识别方法[J].控制与决策,2015,30(12):2153-2160.
[24]董慧,姜赢,高巾,等.基于数字图书馆的本体演化和知识管理研究(Ⅰ)--本体分子理论[J].情报学报,2009,28(3):323-330.
[25]苗壮,张亚非,陆建江.从多个RDFS本体中抽取子本体[J].情报学报,2007,26(1):71-76.
[26]毕强,牟冬梅,范轶.数字图书馆语义互联中的桥本体构建[J].情报学报,2010,29(6):1051-1057.
[27]蔡丽宏,马静.基于综合方法的一种本体映射实验研究[J].情报学报,2010,29(5):820-825.
[28]滕广青,毕强.基于概念格的跨本体映射中概念相似度计算方法[J].情报学报,2012,31(4):390-397.
[29]Li J L,He Z Y,Zhu Q L.An Entropy-based weighted concept lat-tice for merging multi-source geo-ontologies[J].Entropy,2013,15:2303-2318.
[30]Singh S,Cheah Y N.Hybrid approach towards ontology mapping[C]//Proceedings of the International Symposium on Information Technology.IEEE,2010:1490-1493.
[31]王汀,高迎,刘经纬.一种面向中文本体模式的本体对齐框架[J].数据分析与知识发现,2017,1(2):47-57.
[32]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.
[33]黄奇,范佳林,陆佳莹,等.本体映射系统的评价体系研究[J].情报学报,2017,36(8):781-789.
[34]楼雯.馆藏资源语义化关键技术及实证研究[J].中国图书馆学报,2013,39(6):27-40.
[35]Gu J,Xu B,Chen X.An XML query rewriting mechanism with multiple ontologies integration based on complex semantic map-ping[J].Information Fusion,2008,9(4):512-522.
(1)OAEI是一个致力于评价本体合并方法和效率的国际联盟,从2004年开始,该联盟每年都会组织学者、机构参加异构本体整合方法、工作的竞赛,竞赛过程、内容、结果的完整数据和样本都可公开下载。
(1)http://islab.di.unimi.it/content/im_oaei/2016/
(1)Four ontologies.open sources.http://oaei.ontologymatching.org/2016/results/interactive/
(1)https://github.com/ernestojimenezruiz/logmap-matcher
[2]刘晓娟,李广建,化柏林.知识融合:概念辨析与界说[J].图书情报工作,2016,60(13):13-19,32.
[3]林海伦,王元卓,贾岩涛,等.面向网络大数据的知识融合方法综述[J].计算机学报,2017,40(1):1-27.
[4]Ding Y,Foo S.Ontology research and development.Part 2-a re-view of ontology mapping and evolving[J].Journal of Informa-tion Science,2002,28(5):123-136.
[5]Kim J,Kim P,Chung H.Ontology construction using online on-tologies based on selection,mapping and merging[J].Internation-al Journal of Web and Grid Services,2011,7(2):170-189.
[6]Wu Z X,Tian X Y.Research of ontology merging based on con-cept similarity[C]//Proceedings of the Seventh International Con-ference on Measuring Technology and Mechatronics Automation.IEEE,2015:831-834.
[7]于晓繁,王效岳,白如江.本体集成方法和工具综述[J].现代图书情报技术,2011(1):14-21.
[8]Astrova I.Rules for mapping SQL relational databases to OWLontologies[C]//Proceedings of the International Conference on Metadata and Semantics Research.Boston:Springer,2009:415-424.
[9]Gao W,Gao Y,Zhu L.Ranking based ontology learning algo-rithm for similarity measuring and ontology mapping using repre-sentation theory[J].Journal of Information&Optimization Sci-ences,2016,37(2):303-320.
[10]王效岳,胡泽文,白如江,等.本体集成:概念、过程、工具与方法综述[J].图书情报工作,2011,55(16):119-125.
[11]米杨,曹锦丹.基于PROMPT的本体映射实例分析[J].情报学报,2010,29(6):987-991.
[12]Burgun A,Bodenreider O.Mapping the UMLS semantic network into general ontologies[J].Proceedings of AMIA Annual Sympo-sium,2001:86-90.
[13]Mignard C,Nicolle C.Merging BIM and GIS using ontologies application to urban facility management in ACTIVe3D[J].Com-puters in Industry,2014,65(9):1276-1290.
[14]do Amaral M B,Roberts A,Rector A L.NLP techniques associat-ed with the OpenGALEN ontology for semi-automatic textual ex-traction of medical knowledge:abstracting and mapping equiva-lent linguistic and logical constructs[J].Proceedings of AMIA An-nual Symposium,2000:76-80.
[15]徐健,方安,洪娜.一种基于词语相似度计算的本体映射方法[J].现代图书情报技术,2013(2):36-42.
[16]姚晓明,王锋,林兰芬,等.一种高效的多策略本体映射方法[J].中国科技论文,2013,8(7):642-647.
[17]李凯,李万龙,郑山红,等.改进的多策略本体映射方法[J].吉林大学学报(信息科学版),2016,34(4):536-542.
[18]裘江南,李丽冬,吴力文,等.基于传递的语义相关度计算方法研究[J].情报学报,2010,29(4):749-758.
[19]裘江南,李丽冬,吴力文,等.本体中同种语义关系间的可传递规律研究[J].情报学报,2009,28(5):658-663.
[20]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.
[21]于娟,熊振辉,欧忠辉.基于哈斯图的本体偏序关系消冗方法研究[J].情报学报,2015,34(3):279-285.
[22]Maree M,Belkhatir M.Addressing semantic heterogeneity through multiple knowledge base assisted merging of domainspecific ontologies[J].Knowledge-Based Systems,2015,73:199-211.
[23]郭强,关欣,潘丽娜,等.一种基于条件证据网络的多源异类知识融合识别方法[J].控制与决策,2015,30(12):2153-2160.
[24]董慧,姜赢,高巾,等.基于数字图书馆的本体演化和知识管理研究(Ⅰ)--本体分子理论[J].情报学报,2009,28(3):323-330.
[25]苗壮,张亚非,陆建江.从多个RDFS本体中抽取子本体[J].情报学报,2007,26(1):71-76.
[26]毕强,牟冬梅,范轶.数字图书馆语义互联中的桥本体构建[J].情报学报,2010,29(6):1051-1057.
[27]蔡丽宏,马静.基于综合方法的一种本体映射实验研究[J].情报学报,2010,29(5):820-825.
[28]滕广青,毕强.基于概念格的跨本体映射中概念相似度计算方法[J].情报学报,2012,31(4):390-397.
[29]Li J L,He Z Y,Zhu Q L.An Entropy-based weighted concept lat-tice for merging multi-source geo-ontologies[J].Entropy,2013,15:2303-2318.
[30]Singh S,Cheah Y N.Hybrid approach towards ontology mapping[C]//Proceedings of the International Symposium on Information Technology.IEEE,2010:1490-1493.
[31]王汀,高迎,刘经纬.一种面向中文本体模式的本体对齐框架[J].数据分析与知识发现,2017,1(2):47-57.
[32]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.
[33]黄奇,范佳林,陆佳莹,等.本体映射系统的评价体系研究[J].情报学报,2017,36(8):781-789.
[34]楼雯.馆藏资源语义化关键技术及实证研究[J].中国图书馆学报,2013,39(6):27-40.
[35]Gu J,Xu B,Chen X.An XML query rewriting mechanism with multiple ontologies integration based on complex semantic map-ping[J].Information Fusion,2008,9(4):512-522.
(1)OAEI是一个致力于评价本体合并方法和效率的国际联盟,从2004年开始,该联盟每年都会组织学者、机构参加异构本体整合方法、工作的竞赛,竞赛过程、内容、结果的完整数据和样本都可公开下载。
(1)http://islab.di.unimi.it/content/im_oaei/2016/
(1)Four ontologies.open sources.http://oaei.ontologymatching.org/2016/results/interactive/
(1)https://github.com/ernestojimenezruiz/logmap-matcher
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