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RiMOM-IM: A Novel Iterative Framework for Instance Matching

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Abstract

Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM (RiMOM-Instance Matching). The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative (OAEI), and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best.

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References

  1. Shvaiko P, Euzenat J. Ontology matching: State of the art and future challenges. IEEE Trans. Knowl. Data Eng., 2013, 25(1): 158–176.

  2. Ferrara A, Nikolov A, Noessner J et al. Evaluation of instance matching tools: The experience of OAEI. Web Smantics: Science, Services and Agents on the World Wide Web, 2013, 21: 49–60.

  3. Bellahsene Z, Bonifati A, Rahm E. Schema Matching and Mapping. Springer-Verlag Berlin, Heidelberg, 2011.

  4. Huber J, Sztyler T, Noessner J et al. CODI: Combinatorial optimization for data integration—Results for OAEI 2011. In Proc. the 6th International Workshop on Ontology Matching, Oct. 2011, pp.134-141.

  5. Volz J, Bizer C, Gaedke M, Kobilarov G. Discovering and maintaining links on the web data. In Proc. the 8th International Semantic Web Conference, Oct. 2009, pp.650-665.

  6. Suchanek FM, Abiteboul S, Senellart P. PARIS: Probabilistic alignment of relations, instances, and schema. PVLDB, 2011, 5(3): 157–168

  7. Lacoste-Julien S, Palla K, Davies A, Kasneci G, Graepel T, Ghahramani Z. SIGMa: Simple greedy matching for aligning large knowledge bases. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2013, pp.572-580.

  8. Li J, Tang J, Li Y, Luo Q. RiMOM: A dynamic multistrategy ontology alignment framework. IEEE Trans. Knowl. Data Eng., 2009, 21(8): 1218–1232.

  9. Böhm C, de Melo G, Naumann F, Weikum G. LINDA: Distributed web-of-data-scale entity matching. In Proc. the 21st CIKM, Oct.29-Nov.2, 2012, pp.2104-2108.

  10. Diallo G, Ba M. Effective method for large scale ontology matching. In Proc. the 5th SWAT4LS, Nov. 2012.

  11. Li J, Wang Z, Zhang X et al. Large scale instance matching via multiple indexes and candidate selection. Knowledge-Based Systems, 2013, 50: 112–120.

  12. Euzenat J, Valtchev P. Similarity-based ontology alignment in OWL-lite. In Proc. the 16th ECAI, August 2004, pp.333-337.

  13. Jean-Mary Y R, Shironoshita E P, Kabuka M R. Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web, 2009, 7(3): 235–251.

  14. Dragisic Z, Eckert K, Euzenat J et al. Results of the ontology alignment evaluation initiative 2014. In Proc. the 9th International Workshop on Ontology Matching, Oct. 2014, pp.61-104.

  15. Grau B C, Dragisic Z, Eckert K et al. Results of the ontology alignment evaluation initiative 2013. In Proc. the 8th International Workshop on Ontology Matching, Oct. 2013, pp.61-100.

  16. Euzenat J, Ferrara A, van Hage W R et al. Results of the ontology alignment evaluation initiative 2011. In Proc. the 6th Internaitonal Workshop on Ontology Matching, Oct. 2011.

  17. Euzenat J, Ferrara A, Meilicke C et al. Results of the ontology alignment evaluation initiative 2010. In Proc. the 5th International Workshop on Ontology Matching, Nov. 2010.

  18. Do H H, Rahm E. COMA: A system for flexible combination of schema matching approaches. In Proc. the 28th International Conference on Very Large Data Bases, Aug. 2002, pp.610-621.

  19. Nguyen K, Ichise R, Le B. SLINT: A schema-independent linked data interlinking system. In Proc. the 7th International Workshop on Ontology Matching, Nov. 2012.

  20. Hu W, Qu Y. Falcon-AO: A practical ontology matching system. Web Semantics: Science, Services and Agents on the World Wide Web, 2008, 6(3): 237–239

  21. Pirrò G, Talia D. UFOme: An ontology mapping system with strategy prediction capabilities. Data Knowl. Eng., 2010, 69(5): 444–471.

  22. Albagli S, Ben-Eliyahu-Zohary R, Shimony S E. Markov network based ontology matching. InProc. the 21st IJCAI, Jul. 2009, pp.1884-1889.

  23. Melnik S, Garcia-Molina H, Rahm E. Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proc. the 18th ICDE, Feb.26-Mar.1, 2002, pp.117-128.

  24. Ehrig M, Staab S, Sure Y. Bootstrapping ontology alignment methods with APFEL. In Proc. the 18th WWW (Special Interest Tracks and Posters), May 2005, pp.1148-1149.

  25. Doan A, Madhavan J, Dhamankar R, Domingos P, Halevy A Y. Learning to match ontologies on the semantic web. VLDB J, 2003, 12(4): 303–319.

  26. Niepert M, Meilicke C, Stuckenschmidt H. A probabilisticlogical framework for ontology matching. In Proc. the 24th AAAI, Jul. 2010.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Chao Shao, Lin-Mei Hu, Juan-Zi Li, Tonglee Chung & Jun-Bo Xia

  2. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Zhi-Chun Wang

Authors
  1. Chao Shao
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  2. Lin-Mei Hu
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  3. Juan-Zi Li
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  4. Zhi-Chun Wang
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  5. Tonglee Chung
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  6. Jun-Bo Xia
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Correspondence to Lin-Mei Hu.

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Shao, C., Hu, LM., Li, JZ. et al. RiMOM-IM: A Novel Iterative Framework for Instance Matching. J. Comput. Sci. Technol. 31, 185–197 (2016). https://doi.org/10.1007/s11390-016-1620-z

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  • DOI: https://doi.org/10.1007/s11390-016-1620-z

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