Journal on Data Semantics

, Volume 1, Issue 4, pp 219–236

Instance-Based Ontology Matching by Instance Enrichment

  • Balthasar Schopman
  • Shenghui Wang
  • Antoine Isaac
  • Stefan Schlobach
Open Access
Original Article

Abstract

The ontology matching (OM) problem is an important barrier to achieve true Semantic Interoperability. Instance-based ontology matching (IBOM) uses the extension of concepts, the instances directly associated with a concept, to determine whether a pair of concepts is related or not. While IBOM has many strengths it requires instances that are associated with concepts of both ontologies, (i.e) dually annotated instances. In practice, however, instances are often associated with concepts of a single ontology only, rendering IBOM rarely applicable. In this paper we discuss a method that enables IBOM to be used on two disjoint datasets, thus making it far more generically applicable. This is achieved by enriching instances of each dataset with the conceptual annotations of the most similar instances from the other dataset, creating artificially dually annotated instances. We call this technique instance-based ontology matching by instance enrichment (IBOMbIE). We have applied the IBOMbIE algorithm in a real-life use-case where large datasets are used to match the ontologies of European libraries. Existing gold standards and dually annotated instances are used to test the impact and significance of several design choices of the IBOMbIE algorithm. Finally, we compare the IBOMbIE algorithm to other ontology matching algorithms.

Keywords

Ontology matching Semantic Web Semantic interoperability 

References

  1. 1.
    Avesani P, Giunchiglia F, Yatskevich M (2005) A large scale taxonomy mapping evaluation. In: Gil Y, Motta E, Benjamins VR, Musen MA (eds) International semantic web conference. Lecture notes in computer science, vol 3729. Springer, Berlin, pp 67–81. http://dblp.uni-trier.de/rec/bibtex/conf/swws/TodorovG09
  2. 2.
    Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12–19. http://www.bibsonomy.org/bibtex/1f75b35ab969ab89391cf6cbd2176ca67/dblp Google Scholar
  3. 3.
    Caracciolo.C, Euzenat.J, Hollink L et al (2008) Results of the ontology alignment evaluation initiative 2008. In: Proceedings of the 3rd international workshop on ontology matching, collocated with the 7th international semantic web conference (ISWC). http://ceur-ws.org/Vol-431
  4. 4.
    Choi N, Song IY, Han H (2006) A survey on ontology mapping. SIGMOD Record 35(3):34–41. http://dblp.uni-trier.de/db/journals/sigmod/sigmod35.html#ChoiSH06 Google Scholar
  5. 5.
    Doan A, Domingos P, Halevy AY (2003) Learning to match the schemas of data sources: a multistrategy approach. Mach Learn 50(3):279–301. http://dblp.uni-trier.de/db/journals/ml/ml50.html#DoanDH03 Google Scholar
  6. 6.
    Doan A, Madhavan J, Domingos P, Halevy A (2004) Ontology matching: a machine learning approach. In: Handbook on ontologies in information systems. Springer, Berlin, pp 397–416Google Scholar
  7. 7.
    Euzenat J, Meilicke C, Stuckenschmidt H, Shvaiko P, dos Santos CT (2011) Ontology alignment evaluation initiative: six years of experience. J Data Semant 15: 158–192CrossRefGoogle Scholar
  8. 8.
    Euzenat J, Shvaiko P (2007) Ontology matching. Springer-Verlag, Heidelberg (DE), p 341. ISBN 3-540-49611-4Google Scholar
  9. 9.
    Euzenat J, Valtchev P (2004) Similarity-based ontology alignment in owl-lite. In: de Mántaras RL, Saitta L (eds) ECAI. IOS Press, Amsterdam, pp 333–337Google Scholar
  10. 10.
    Ferrara A, Nikolov A, Scharffe F (2011) Data linking for the semantic web. Int J Semant Web Inf Syst 7(3): 46–76CrossRefGoogle Scholar
  11. 11.
    Hamdi F, Zargayouna H, Safar B, Reynaud C (2008) Taxomap in the oaei alignment contest. In: Shvaiko P, Euzenat J, Giunchiglia F, Stuckenschmidt H (eds) OM, CEUR workshop proceedings, vol 431. CEUR-WS.org. http://dblp.uni-trier.de/db/conf/semweb/om2008.html#HamdiZSR08
  12. 12.
    Hoshiai T, Yamane Y, Nakamura D, Tsuda H (2004) A semantic category matching approach to ontology alignment. In: Proceedings of the third international workshop on evaluation of ontology-based tools (EON)Google Scholar
  13. 13.
    Ichise R, Takeda H, Honiden S (2003) Integrating multiple internet directories by instance-based learning. In: Proceedings of the eighteenth international joint conference on artificial intelligenceGoogle Scholar
  14. 14.
    Isaac A, Matthezing H, van der Meij L, Schlobach S, Wang S, Zinn C (2008) Putting ontology alignment in context: usage scenarios, deployment and evaluation in a library case. In: Hauswirth M, Koubarakis M, Bechhofer S (eds) Proceedings of the 5th European semantic web conference, LNCS. Springer, Berlin. http://data.semanticweb.org/conference/eswc/2008/papers/188
  15. 15.
    Isaac A, vann der Meij L, Schlobach S, Wang S (2007) An empirical study of instance-based ontology matching. In: ISWC/ASWC, pp 253–266Google Scholar
  16. 16.
    Isaac A, Wang S, Zinn C, Matthezing H, van der Meij L, Schlobach S (2009) Evaluating thesaurus alignments for semantic interoperability in the library domain. IEEE Intell Syst 24(2): 76–86CrossRefGoogle Scholar
  17. 17.
    Kopcke H, Rahm E (2010) Frameworks for entity matching: a comparison. Data Knowl Eng 69: 197–210 doi:10.1016/j.datak.2009.10.003 CrossRefGoogle Scholar
  18. 18.
    Leme LAPP, Casanova MA, Breitman KK, Furtado AL (2009) Instance-based owl schema matching. In: Filipe J, Cordeiro J (eds) Enterprise information systems, Proceedings of 11th international conference, ICEIS 2009, Milan, May 6–10. Lecture notes in business information processing, vol 24. Springer, Berlin, pp 14–26. doi:10.1007/978-3-642-01347-8_2
  19. 19.
    Li J, Tang J, Li Y, Luo Q (2009) Rimom: a dynamic multistrategy ontology alignment framework. IEEE Trans Knowl Eng 21: 1218–1232 doi:10.1109/TKDE.2008.202 CrossRefGoogle Scholar
  20. 20.
    Li WS, Clifton C, Liu SY (2000) Database integration using neural networks: implementation and experiences. Knowl Inf Syst 2: 73–96MATHCrossRefGoogle Scholar
  21. 21.
    Maedche A, Motik B, Silva N, Volz R (2002) Mafra—a mapping framework for distributed ontologies. In: Gmez-Prez A, Benjamins VR (eds) EKAW. Lecture notes in computer science, vol 2473. Springer, Berlin, pp 235–250. http://dblp.uni-trier.de/db/conf/ekaw/ekaw2002.html#MaedcheMSV02
  22. 22.
    Nagy M, Vargas-Vera M, Stolarski P, Motta E (2008) DSSim results for OAEI 2008. http://ceur-ws.org/Vol-431/oaei08_paper5.pdf
  23. 23.
    Rahm E (2011) Towards large-scale schema and ontology matching. ReCALL 5:1–26. http://www.springerlink.com/index/M5055K8721752228.pdf Google Scholar
  24. 24.
    Rahm E, Bernstein PA (2001) A survey of approaches to automatic schema matching. VLDB J 10(4): 334–350MATHCrossRefGoogle Scholar
  25. 25.
    Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11): 613–620 doi:10.1145/361219.361220 MATHCrossRefGoogle Scholar
  26. 26.
    Schopman B (2009) Instance-based ontology matching by instance enrichment. Master’s thesis, Vrije Universiteit, The NetherlandsGoogle Scholar
  27. 27.
    Schopman B, Wang S, Schlobach S (2008) Deriving concept mappings through instance mappings. In: ASWCGoogle Scholar
  28. 28.
    Spero SE (2011) What, if anything, is a subdivision? In: International Society for Knowledge OrganisationGoogle Scholar
  29. 29.
    Stumme G, Maedche A (2001) Fca-merge: bottom-up merging of ontologies. In: Proceedings of the 17th international conference on artificial intelligence (IJCAI ’01), Seattle, pp 225–230Google Scholar
  30. 30.
    Thor A, Kirsten T, Rahm E (2007) Instance-based matching of hierarchical ontologies. In: Kemper A, Schning H, Rose T, Jarke M, Seidl T, Quix C, Brochhaus C (eds) BTW, LNI, GI, vol 103, pp 436–448. http://dblp.uni-trier.de/db/conf/btw/btw2007.html#ThorKR07
  31. 31.
    Todorov K, Geibel P (2009) Variable selection as an instance-based ontology mapping strategy. In: Arabnia HR, Marsh A (eds) SWWS. CSREA Press, USA, pp 3–9Google Scholar
  32. 32.
    Todorov K, Geibel P, Kuhnberger KU (2010) Mining concept similarities for heterogeneous ontologies. In: Perner P (ed) ICDM. Lecture notes in computer science, vol 6171. Springer, Berlin, pp 86–100Google Scholar
  33. 33.
    Udrea O, Getoor L, Miller RJ (2007) Leveraging data and structure in ontology integration. In: Proceedings of the ACM SIGMOD international conference on management of data, Beijing, June 12–14, pp 449–460Google Scholar
  34. 34.
    Wang P, Xu B (2008) Lily: ontology alignment results for OAEI 2008. In: Shvaiko P, Euzenat J, Giunchiglia F, Stuckenschmidt H (eds) OM, Proceedings of the 3rd international workshop on ontology matching (OM-2008), collocated with the 7th international semantic web conference (ISWC-2008), Karlsruhe, Germany, 26 October 2008, vol 431. CEUR-WS.org. http://ceur-ws.org/Vol-431/oaei08_paper7.pdf
  35. 35.
    Wang S, Englebienne G, Schlobach S (2008) Learning concept mappings from instance similarity. In: International semantic web conference, pp 339–355Google Scholar
  36. 36.
    Wang S, Isaac A, Schlobach S, van der Meij L, Schopman BAC (2012) Instance-based semantic interoperability in the cultural heritage. Semantic Web 3(1): 45–64Google Scholar
  37. 37.
    Wang S, Isaac A Schopman B, Schlobach S, van der Meij L (2009) Matching multi-lingual subject vocabularies. In: Proceedings of the 13th European Conference on Digital Libraries (ECDL2009)Google Scholar
  38. 38.
    Wartena C, Brussee R (2008) Instanced-based mapping between thesauri and folksonomies. In: ISWC’08Google Scholar
  39. 39.
    Zaiss KS (2010) Instance-based ontology matching and the evaluation of matching systems. Ph.D. thesis, Heinrich Heine Universität DüsseldorfGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Balthasar Schopman
    • 1
  • Shenghui Wang
    • 1
  • Antoine Isaac
    • 1
  • Stefan Schlobach
    • 1
  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands

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