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Towards Matching of Domain-Specific Schemas Using General-Purpose External Background Knowledge

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The Semantic Web: ESWC 2020 Satellite Events (ESWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12124))

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Abstract

Schema matching is an important and time consuming part within the data integration process. Yet, it is rarely automatized – particularly in the business world. In recent years, the amount of freely available structured knowledge has grown exponentially. Large knowledge graphs such as BabelNet, DBnary (Wiktionary in RDF format), DBpedia, or Wikidata are available. However, these knowledge bases are hardly exploited for automated matching. One exception is the biomedical domain: Here domain-specific background knowledge is broadly available and heavily used with a focus on reusing existing alignments and on exploiting larger, domain-specific mediation ontologies. Nonetheless, outside the life sciences domain such specialized structured resources are rare. In terms of general knowledge, few background knowledge sources are exploited except for WordNet. In this paper, we present our research idea towards further exploiting general-purpose background knowledge within the schema matching process. An overview of the state of the art is given and we outline how our proposed research approach fits in. Potentials and limitations are discussed and we summarize our intermediate findings.

Category: Early Stage Ph.D.

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Notes

  1. 1.

    In 2013, Euzenat and Shvaiko  [7] counted more than 80 schema matching systems that exploit WordNet.

  2. 2.

    Note that the semantic expressiveness or quality of the generated technical ontologies is only as good as the inputs for the transformation and influences the results of automated matching methods. However, the outlined approach is also used for semantically richer models such as conceptual data models that are frequently used in the financial services industry, for instance.

  3. 3.

    http://kgvec2go.org/.

References

  1. Annane, A., Bellahsene, Z., Azouaou, F., Jonquet, C.: Selection and combination of heterogeneous mappings to enhance biomedical ontology matching. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 19–33. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_2

    Chapter  Google Scholar 

  2. Basel Committee on Banking Supervision: Principles for Effective Risk Data Aggregation and Risk Reporting. Bank for International Settlements, Basel (2013)

    Google Scholar 

  3. Chen, X., Xia, W., Jiménez-Ruiz, E., Cross, V.V.: Extending an ontology alignment system with bioportal: a preliminary analysis. In: Proceedings of the ISWC 2014 Posters & Demonstrations Track a track within the 13th International Semantic Web Conference, ISWC 2014, Riva del Garda, Italy, October 21, 2014. CEUR Workshop Proceedings, vol. 1272, pp. 313–316. CEUR-WS.org (2014)

    Google Scholar 

  4. David, J., Euzenat, J., Scharffe, F., dos Santos, C.T.: The alignment API 4.0. Semant. Web 2(1), 3–10 (2011)

    Google Scholar 

  5. Doan, A., Halevy, A., Ives, Z.: Principles of Data Integration, Chap. 1, p. 6. Morgan Kaufmann, Burlington (2012)

    Google Scholar 

  6. Euzenat, J., Meilicke, C., Stuckenschmidt, H., Shvaiko, P., dos Santos, C.T.: Ontology alignment evaluation initiative: six years of experience. J. Data Semant. 15, 158–192 (2011)

    Article  Google Scholar 

  7. Euzenat, J., Shvaiko, P.: Ontology Matching, Chap. 13, 2nd edn. Springer, New York (2013). https://doi.org/10.1007/978-3-642-38721-0

    Book  MATH  Google Scholar 

  8. Fahad, M.: ER2OWL: generating OWL ontology from ER diagram. In: Shi, Z., Mercier-Laurent, E., Leake, D. (eds.) IIP 2008. ITIFIP, vol. 288, pp. 28–37. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-87685-6_6

    Chapter  Google Scholar 

  9. Faria, D., Pesquita, C., Santos, E., Cruz, I.F., Couto, F.M.: Automatic background knowledge selection for matching biomedical ontologies. PLoS ONE 9(11), e111226 (2014)

    Article  Google Scholar 

  10. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge (1998)

    Google Scholar 

  11. Groenfeldt, T.: Taming the high costs of compliance with tech (2018). https://www.forbes.com/sites/tomgroenfeldt/2018/03/22/taming-the-high-costs-of-compliance-with-tech/

  12. Groß, A., Hartung, M., Kirsten, T., Rahm, E.: Mapping composition for matching large life science ontologies. In: Proceedings of the 2nd International Conference on Biomedical Ontology, Buffalo, NY, USA, 26–30 July, 2011. CEUR Workshop Proceedings, vol. 833. CEUR-WS.org (2011)

    Google Scholar 

  13. Hartung, M., Gross, A., Kirsten, T., Rahm, E.: Effective composition of mappings for matching biomedical ontologies. In: Simperl, E., et al. (eds.) ESWC 2012. LNCS, vol. 7540, pp. 176–190. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46641-4_13

    Chapter  Google Scholar 

  14. Hertling, S., Paulheim, H.: WikiMatch - using wikipedia for ontology matching. In: Shvaiko, P., Euzenat, J., Kementsietsidis, A., Mao, M., Noy, N., Stuckenschmidt, H. (eds.) OM-2012: Proceedings of the ISWC Workshop, vol. 946, pp. 37–48 (2012)

    Google Scholar 

  15. Hertling, S., Paulheim, H.: WebIsALOD: providing hypernymy relations extracted from the web as linked open data. In: d’Amato, C., et al. (eds.) ISWC 2017, Part II. LNCS, vol. 10588, pp. 111–119. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_11

    Chapter  Google Scholar 

  16. Hertling, S., Portisch, J., Paulheim, H.: MELT - matching evaluation toolkit. In: Proceedings of the Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, 9–12 September 2019 , pp. 231–245 (2019)

    Google Scholar 

  17. Jiménez-Ruiz, E., Cuenca Grau, B.: LogMap: logic-based and scalable ontology matching. In: Aroyo, L., et al. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 273–288. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_18

    Chapter  Google Scholar 

  18. Kachroudi, M., Diallo, G., Yahia, S.B.: KEPLER at OAEI 2018. In: Proceedings of the 13th International Workshop on Ontology Matching co-located with the 17th International Semantic Web Conference. CEUR Workshop Proceedings, vol. 2288, pp. 173–178. CEUR-WS.org (2018)

    Google Scholar 

  19. Lehmann, J., et al.: Dbpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)

    Article  Google Scholar 

  20. Lin, F., Krizhanovsky, A.: Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint. CoRR abs/1109.0732 (2011)

    Google Scholar 

  21. Lin, F., Sandkuhl, K., Xu, S.: Context-based ontology matching: concept and application cases. J. UCS 18(9), 1093–1111 (2012)

    Google Scholar 

  22. Mohammadi, M., Atashin, A.A., Hofman, W., Tan, Y.: Comparison of ontology alignment systems across single matching task via the McNemar’s test. TKDD 12(4), 51:1–51:18 (2018)

    Article  Google Scholar 

  23. Nickel, M., Tresp, V., Kriegel, H.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June–2 July 2011, pp. 809–816. Omnipress (2011)

    Google Scholar 

  24. Paulheim, H.: Wesee-match results for OEAI 2012. In: Proceedings of the 7th International Workshop on Ontology Matching, Boston, MA, USA, 11 November 2012. CEUR Workshop Proceedings, vol. 946. CEUR-WS.org (2012)

    Google Scholar 

  25. Portisch, J., Hertling, S., Paulheim, H.: Visual analysis of ontology matching results with the MELT dashboard. In: The Semantic Web: ESWC 2020 Satellite Events (2020, to appear)

    Google Scholar 

  26. Portisch, J., Hladik, M., Paulheim, H.: Evaluating ontology matchers on real-world financial services data models. In: Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems (SEMANTiCS 2019), Karlsruhe, Germany, September 9–12 2019. CEUR Workshop Proceedings, vol. 2451. CEUR-WS.org (2019)

    Google Scholar 

  27. Portisch, J., Hladik, M., Paulheim, H.: Wiktionary matcher. In: Proceedings of the 14th International Workshop on Ontology Matching co-located with the 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, 26 October 2019. CEUR Workshop Proceedings, vol. 2536, pp. 181–188. CEUR-WS.org (2019)

    Google Scholar 

  28. Portisch, J., Hladik, M., Paulheim, H.: KGvec2go - knowledge graph embeddings as a service. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC), Marseille, France (2020, to appear)

    Google Scholar 

  29. Portisch, J., Paulheim, H.: ALOD2Vec matcher. In: Proceedings of the 13th International Workshop on Ontology Matching Co-located with the 17th International Semantic Web Conference, pp. 132–137 (2018)

    Google Scholar 

  30. Portisch, J.P.: Automatic schema matching utilizing hypernymy relations extracted from the web (2018). https://madoc.bib.uni-mannheim.de/52029/

  31. Quix, C., Roy, P., Kensche, D.: Automatic selection of background knowledge for ontology matching. In: Proceedings of the International Workshop on Semantic Web Information Management, SWIM 2011, Athens, Greece, 12 June 2011, p. 5. ACM (2011)

    Google Scholar 

  32. Ristoski, P., Rosati, J., Noia, T.D., Leone, R.D., Paulheim, H.: Rdf2vec: RDF graph embeddings and their applications. Semant. Web 10(4), 721–752 (2019)

    Article  Google Scholar 

  33. Sérasset, G.: DBnary: Wiktionary as a lemon-based multilingual lexical resource in RDF. Semant. Web 6(4), 355–361 (2015)

    Article  Google Scholar 

  34. Wang, X., Haas, L.M., Meliou, A.: Explaining data integration. IEEE Data Eng. Bull. 41(2), 47–58 (2018)

    Google Scholar 

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Acknowledgements

I would like to thank my supervisor, Prof. Heiko Paulheim, for his valuable feedback, guidance, and support in the realization of this work.

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Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Jan Philipp Portisch

  2. SAP SE Product Engineering Financial Services, Walldorf, Germany

    Jan Philipp Portisch

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  1. Jan Philipp Portisch
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Correspondence to Jan Philipp Portisch .

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Editors and Affiliations

  1. University of Erlangen-Nuremberg, Nuremberg, Germany

    Andreas Harth

  2. National Research Council, Rome, Italy

    Valentina Presutti

  3. Eurecom, Sophia Antipolis, France

    Raphaël Troncy

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Maribel Acosta

  5. Institute for Information Business at WU Wien, Vienna, Austria

    Axel Polleres

  6. Institute for Information Business at WU Wien, Vienna, Austria

    Javier D. Fernández

  7. Siemens AG Österreich, Vienna, Austria

    Josiane Xavier Parreira

  8. Linköping University, Linköping, Sweden

    Olaf Hartig

  9. Aalborg University, Aalborg, Denmark

    Katja Hose

  10. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Michael Cochez

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Portisch, J.P. (2020). Towards Matching of Domain-Specific Schemas Using General-Purpose External Background Knowledge. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_42

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  • DOI: https://doi.org/10.1007/978-3-030-62327-2_42

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