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KnoE:A Web Mining Tool to Validate Previously Discovered Semantic Correspondences 被引量:1

KnoE:A Web Mining Tool to Validate Previously Discovered Semantic Correspondences
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摘要 The problem of matching schemas or ontologies consists of providing corresponding entities in two or more knowledge models that belong to a same domain but have been developed separately. Nowadays there are a lot of techniques and tools for addressing this problem, however, the complex nature of the matching problem make existing solutions for real situations not fully satisfactory. The Google Similarity Distance has appeared recently. Its purpose is to mine knowledge from the Web using the Google search engine in order to semantically compare text expressions. Our work consists of developing a software application for validating results discovered by schema and ontolog2/ matching tools using the philosophy behind this distance. Moreover, we are interested in using not only Google, but other popular search engines with this similarity distance. The results reveal three main facts. Firstly, some web search engines can help us to validate semantic correspondences satisfactorily. Secondly there are significant differences among the web search engines. And thirdly the best results are obtained when using combinations of the web search engines that we have studied. The problem of matching schemas or ontologies consists of providing corresponding entities in two or more knowledge models that belong to a same domain but have been developed separately. Nowadays there are a lot of techniques and tools for addressing this problem, however, the complex nature of the matching problem make existing solutions for real situations not fully satisfactory. The Google Similarity Distance has appeared recently. Its purpose is to mine knowledge from the Web using the Google search engine in order to semantically compare text expressions. Our work consists of developing a software application for validating results discovered by schema and ontolog2/ matching tools using the philosophy behind this distance. Moreover, we are interested in using not only Google, but other popular search engines with this similarity distance. The results reveal three main facts. Firstly, some web search engines can help us to validate semantic correspondences satisfactorily. Secondly there are significant differences among the web search engines. And thirdly the best results are obtained when using combinations of the web search engines that we have studied.
出处 《Journal of Computer Science & Technology》 SCIE EI CSCD 2012年第6期1222-1232,共11页 计算机科学技术学报(英文版)
基金 supported by Spanish Ministry of Innovation and Science through REALIDAD:Gestion,Analisis y Explotacion Eficiente de Datos Vinculados under Grant No.TIN2011-25840
关键词 database integration data and knowledge engineering similarity distance database integration, data and knowledge engineering, similarity distance
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  • 1Berners-Lee T, Hendler J, Lassila O. The Semantic Web. Sci- entific American, 2001, 284(5): 34-43.
  • 2Euzenat J, Shvalko P. Ontology Matching, Springer, 2007.
  • 3Kiefer C, Bernstein A, Stocker M. The fundamentals of iS- PARQL: A virtual triple approach for similarity-based seman- tic web tasks. In Proc. ISWC/ASWC, Nov. 2007, pp.295- 309.
  • 4Ziegler P, Kiefer C, Sturm C, Dittrich K R, Bernstein A. Detecting similarities in ontologies with the SOQA-SimPack toolkit. In Proc. the 10th EDBT, March 2006, pp.59-76.
  • 5Lambrix P, Tan H. A tool for evaluating ontology alignment strategies. J. Data Semantics, 2007, 8: 182-202.
  • 6Domshlak C, Gal A, Roitman H. Rank aggregation for auto- matic schema matching. IEEE Trans. Knowl. Data Eng., 2007, 19(4): 538-553.
  • 7Gal A, Anaby-Tavor A, Trombetta A, Montesi D. A frame- work for modeling and evaluating automatic semantic recon- ciliation. VLDB Journal, 2005, 14(1): 50-67.
  • 8Ehrig M, Staab S, Sure Y. Bootstrapping ontology alignment methods with APFEL. In Proc. the 4th International Seman- tic Web Conference, Nov. 2005, pp.186-200.
  • 9Lee Y, Sayyadian M, Doan A, Rosenthal A S. eTuner: Tuning schema matching software using synthetic scenarios. VLDB Journal, 2007, 16(1): 97-122.
  • 10Mao M, Peng Y, Spring M. An adaptive ontology mapping approach with neural network based constraint satisfaction.J. Web Semantics, 2010, 8(1): 14-25.

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