Heuristics for Connecting Heterogeneous Knowledge via FrameBase

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)


With recent advances in information extraction techniques, various large-scale knowledge bases covering a broad range of knowledge have become publicly available. As no single knowledge base covers all information, many applications require access to integrated knowledge from multiple knowledge bases. Achieving this, however, is challenging due to differences in knowledge representation. To address this problem, this paper proposes to use linguistic frames as a common representation and maps heterogeneous knowledge bases to the FrameBase schema, which is formed by a large inventory of these frames. We develop several methods to create complex mappings from external knowledge bases to this schema, using text similarity measures, machine learning, and different heuristics. We test them with different widely used large-scale knowledge bases, YAGO2s, Freebase and WikiData. The resulting integrated knowledge can then be queried in a homogeneous way.


  1. 1.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD 2008, pp. 1247–1250 (2008)Google Scholar
  2. 2.
    Collins, M.: Head-driven statistical models for natural language parsing. Comput. Linguist. 29(4), 589–637 (2003)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    David, J., Euzenat, J., Scharffe, F., dos Santos, C.T.: The alignment API 4.0. Semant. Web J. 2(1), 3–10 (2011)Google Scholar
  4. 4.
    Dhamankar, R., Lee, Y., Doan, A.H., Halevy, A., Domingos, P.: iMAP: discovering complex semantic matches between database schemas. SIGMOD 2004, 383–394 (2004)Google Scholar
  5. 5.
    Doan, A., Halevy, A.Y., Ives, Z.G.: Principles of Data Integration. Morgan Kaufmann, Burlington (2012)Google Scholar
  6. 6.
    Dragisic, Z., et al.: Results of the ontology alignment evaluation initiative 2014. In: OM 2014, pp. 61–104 (2014)Google Scholar
  7. 7.
    Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: a survey. IEEE TKDE 19(1), 1–16 (2007)Google Scholar
  8. 8.
    Erxleben, F., Günther, M., Krötzsch, M., Mendez, J., Vrandečić, D.: Introducing Wikidata to the linked data web. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 50–65. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Berlin (2007)MATHGoogle Scholar
  10. 10.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)MATHGoogle Scholar
  11. 11.
    Fillmore, C.J., Johnson, C.R., Petruck, M.R.L.: Background to framenet. Int. J. Lexicogr. 16(3), 235–250 (2003)CrossRefGoogle Scholar
  12. 12.
    Galárraga, L., Heitz, G., Murphy, K., Suchanek, F.M.: Canonicalizing open knowledge bases. In: CIKM 2014, pp. 1679–1688 (2014)Google Scholar
  13. 13.
    Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-match: an algorithm and an implementation of semantic matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Harris, S., Seaborne, A.: SPARQL 1.1 Query Language. W3C Recommendation, W3C Consortium, March 2013Google Scholar
  15. 15.
    Hayes, P.: RDF Semantics. Technical report, W3C Consortium (2004). http://www.w3.org/TR/2004/REC-rdf-mt-20040210/
  16. 16.
    Kalyanpur, A., et al.: Structured data, inference in DeepQA. IBM J. Res. Dev. 56(3.4), 10:1–10:14 (2012)Google Scholar
  17. 17.
    Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: ACL 2003, pp. 423–430 (2003)Google Scholar
  18. 18.
    Mongiovì, M., Recupero, D.R., Gangemi, A., Presutti, V., Nuzzolese, A.G., Consoli, S.: Semantic reconciliation of knowledge extracted from text through a novel machine reader. In: K-CAP 2015, pp. 25:1–25:4 (2015)Google Scholar
  19. 19.
    Pilehvar, M.T., Jurgens, D., Navigli, R.: Align, disam-biguate and walk: a unified approach for measuring semantic similarity. In: ACL 2013, pp. 1341–1351 (2013)Google Scholar
  20. 20.
    Ritze, D., Meilicke, C., Svb-Zamazal, O., Stuckenschmidt, H.: A pattern-based ontology matching approach for detecting complex correspondences. In: OM 2010 (2008)Google Scholar
  21. 21.
    Rouces, J., de Melo, G., Hose, K.: FrameBase: representing n-ary relations using semantic frames. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 505–521. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  22. 22.
    Rouces, J., de Melo, G., Hose, K.: Representing specialized events with FrameBase. In: DeRiVE 2015 (2015)Google Scholar
  23. 23.
    Scharffe, F., Fensel, D.: Correspondence patterns for ontology alignment. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 83–92. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Suchanek, F.M., Abiteboul, S., Senellart, P.: PARIS: probabilistic alignment of relations, instances, and schema. PVLDB 5(3), 157–168 (2011)Google Scholar
  25. 25.
    Suchanek, F.M., Hoffart, J., Kuzey, E., Lewis-Kelham, E.: YAGO2s: modular high-quality information extraction with an application to flight planning. In: BTW, pp. 515–518 (2013)Google Scholar
  26. 26.
    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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Aalborg UniversityAalborgDenmark
  2. 2.Tsinghua UniversityBeijingChina

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