Article

A machine learning approach for instance matching based on similarity metrics

Authors:

Evan Wei Xiang,

Yong YuAuthors Info & Claims

ISWC'12: Proceedings of the 11th international conference on The Semantic Web - Volume Part I

Pages 460 - 475

https://doi.org/10.1007/978-3-642-35176-1_29

Published: 11 November 2012 Publication History

Abstract

The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.

References

[1]

Ananthakrishna, R., Chaudhuri, S., Ganti, V.: Eliminating fuzzy duplicates in data warehouses. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 586-597. VLDB Endowment (2002)

Digital Library

[2]

Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Machine Learning 56(1), 89-113 (2004)

Digital Library

[3]

Bilenko, M., Mooney, R., Cohen, W., Ravikumar, P., Fienberg, S.: Adaptive name matching in information integration. IEEE Intelligent Systems 18(5), 16-23 (2003)

Digital Library

[4]

Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. International Journal on Semantic Web and Information Systems (IJSWIS) 5(3), 1-22 (2009)

[5]

Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001)

Digital Library

[6]

Cochinwala, M., Kurien, V., Lalk, G., Shasha, D.: Efficient data reconciliation. Information Sciences 137(1), 1-15 (2001)

Digital Library

[7]

Cohen, W.: Integration of heterogeneous databases without common domains using queries based on textual similarity. ACM SIGMOD Record 27, 201-212 (1998)

Digital Library

[8]

Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193-200. ACM (2007)

Digital Library

[9]

Eaton, E., desJardins, M., et al.: Selective transfer between learning tasks using task-based boosting. In: Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)

[10]

Ell, B., Vrande?cić, D., Simperl, E.: Labels in the Web of Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 162-176. Springer, Heidelberg (2011)

Digital Library

[11]

Elmagarmid, A., Ipeirotis, P., Verykios, V.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19(1), 1-16 (2007)

Digital Library

[12]

Freund, Y., Schapire, R.: A Desicion-Theoretic Generalization of On-line Learning and an Application to Boosting. In: Vitányi, P. M. B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23-37. Springer, Heidelberg (1995)

Digital Library

[13]

Guha, S., Koudas, N., Marathe, A., Srivastava, D.: Merging the results of approximate match operations. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 636-647. VLDB Endowment (2004)

Digital Library

[14]

Hogan, A., Harth, A., Decker, S.: Performing object consolidation on the semantic web data graph (2007)

[15]

Hogan, A., Polleres, A., Umbrich, J., Zimmermann, A.: Some entities are more equal than others: statistical methods to consolidate linked data. In: 4th International Workshop on New Forms of Reasoning for the Semantic Web: Scalable and Dynamic, NeFoRS 2010 (2010)

[16]

Hu, W., Chen, J., Cheng, G., Qu, Y.: Objectcoref & falcon-ao: results for oaei 2010. In: Ontology Matching, p. 158 (2010)

[17]

Hu, W., Chen, J., Qu, Y.: A self-training approach for resolving object coreference on the semantic web. In: Proceedings of the 20th International Conference on World Wide Web, pp. 87-96. ACM (2011)

Digital Library

[18]

Isele, R., Jentzsch, A., Bizer, C.: Efficient multidimensional blocking for link discovery without losing recall (2011)

[19]

Li, J., Tang, J., Li, Y., Luo, Q.: Rimom: A dynamic multistrategy ontology alignment framework. IEEE Transactions on Knowledge and Data Engineering 21(8), 1218-1232 (2009)

Digital Library

[20]

Loh, W.: Classification and regression tree methods. In: Encyclopedia of Statistics in Quality and Reliability (2008)

[21]

Ngomo, A., Auer, S.: Limes-a time-efficient approach for large-scale link discovery on the web of data. In: Proceedings of IJCAI (2011)

Digital Library

[22]

Ngomo, A., Lehmann, J., Auer, S., Höffner, K.: Raven-active learning of link specifications. In: Ontology Matching, p. 25 (2011)

[23]

Niu, X., Rong, S., Zhang, Y., Wang, H.: Zhishi.links results for oaei 2011. In: Ontology Matching, p. 220 (2011)

[24]

Pan, S., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345-1359 (2010)

Digital Library

[25]

Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity uncertainty and citation matching. In: Proceedings of NIPS 2002 (2002)

[26]

Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4), 334-350 (2001)

Digital Library

[27]

Raimond, Y., Sutton, C., Sandler, M.: Automatic interlinking of music datasets on the semantic web. In: Proceedings of the 1st Workshop about Linked Data on the Web. Citeseer (2008)

[28]

Sleeman, J., Finin, T.: Computing foaf co-reference relations with rules and machine learning. In: Proceedings of the Third International Workshop on Social Data on the Web (2010)

[29]

Sleeman, J., Finin, T.: A machine learning approach to linking foaf instances. In: Spring Symposium on Linked Data Meets AI. AAAI (January 2010)

[30]

Song, D., Heflin, J.: Automatically Generating Data Linkages Using a Domain-Independent Candidate Selection Approach. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 649-664. Springer, Heidelberg (2011)

Digital Library

[31]

Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and Maintaining Links on the Web of Data. In: Bernstein, A., Karger, D. R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 650-665. Springer, Heidelberg (2009)

Digital Library

[32]

Wang, Z., Zhang, X., Hou, L., Zhao, Y., Li, J., Qi, Y., Tang, J.: Rimom results for oaei 2010. In: Ontology Matching, p. 195 (2010)

[33]

Winkler, W.: The state of record linkage and current research problems. In: Statistical Research Division, US Census Bureau. Citeseer (1999)

[34]

Winkler, W.: Overview of record linkage and current research directions. In: Bureau of the Census. Citeseer (2006)

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Christophides VEfthymiou VPalpanas TPapadakis GStefanidis K(2020)An Overview of End-to-End Entity Resolution for Big DataACM Computing Surveys10.1145/341889653:6(1-42)Online publication date: 6-Dec-2020
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A machine learning approach for instance matching based on similarity metrics
1. Computing methodologies
  1. Machine learning

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Published In

cover image Guide Proceedings

ISWC'12: Proceedings of the 11th international conference on The Semantic Web - Volume Part I

November 2012

673 pages

ISBN:9783642351754

Editors:
Philippe Cudré-Mauroux
University of Fribourg, Switzerland
,
Jeff Heflin
Lehigh University, Bethlehem, PA, Switzerland
,
Evren Sirin
Clark & Parsia, Washington, DC, PA, Switzerland
,
Tania Tudorache
Stanford University, Washington, DC, CA, Switzerland
,
Jérôme Euzenat
INRIA & LIG, Le Cesnay Cedex, CA, France

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Elsevier
Yahoo! Research
Artificial Intelligence Journal
ORACLE: ORACLE
IBM: IBM

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 November 2012

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Cited By

Christophides VEfthymiou VPalpanas TPapadakis GStefanidis K(2020)An Overview of End-to-End Entity Resolution for Big DataACM Computing Surveys10.1145/341889653:6(1-42)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.1145/3418896
Idrissou AZamborlini VVan Harmelen FLatronico CKejriwal MSzekely PTroncy R(2019)Contextual Entity Disambiguation in Domains with Weak Identity CriteriaProceedings of the 10th International Conference on Knowledge Capture10.1145/3360901.3364440(259-262)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3360901.3364440
Zhang FLiu XTang JDong YYao PZhang JGu XWang YShao BLi RWang KTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)OAGProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330785(2585-2595)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330785
Aydar MAyvaz S(2019)An improved method of locality-sensitive hashing for scalable instance matchingKnowledge and Information Systems10.1007/s10115-018-1199-558:2(275-294)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10115-018-1199-5
Balaji JMin CJaved FZhu Y(2018)AvatarProceedings of the Second Workshop on Data Management for End-To-End Machine Learning10.1145/3209889.3209892(1-10)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1145/3209889.3209892
Ochieng PKyanda S(2018)A statistically-based ontology matching toolDistributed and Parallel Databases10.1007/s10619-017-7206-036:1(195-217)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10619-017-7206-0
Lu WDai HZhang ZWu CZhuang Y(2018)Active instance matching with pairwise constraints and its application to Chinese knowledge base constructionKnowledge and Information Systems10.1007/s10115-017-1076-755:1(171-214)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10115-017-1076-7
Nguyen KIchise R(2017)ScLinkJournal of Intelligent Information Systems10.1007/s10844-016-0426-348:3(519-551)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10844-016-0426-3
Sagi TGal ABarkol OBergman RAvram AÖzcan FKoutrika GMadden S(2016)Multi-Source Uncertain Entity Resolution at Yad VashemProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2903737(807-819)Online publication date: 26-Jun-2016
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Achichi MBen Ellefi MSymeonidou DTodorov K(2016)Automatic Key Selection for Data LinkingKnowledge Engineering and Knowledge Management10.1007/978-3-319-49004-5_1(3-18)Online publication date: 19-Nov-2016
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