Text mining services can be used to extract and categorize entities from textual information on the web. Merging results from multiple services could improve extraction quality. This requires to have an integrated extraction taxonomy and corresponding mappings between individual taxonomies that are used for categorizing extracted information. However, current ontology matching approaches cannot be applied since the available meta data within most taxonomies is weak.

In this article we propose a novel taxonomy alignment process that allows us to automatically identify equal, hierarchical and associative mappings and integrate those mappings in a global taxonomy. We broadly evaluate our matching approach on real world service taxonomies and compare to state-of-the-art approaches.


Instance-based matching Text mining Taxonomy alignment 


  1. 1.
    Grimes, S.: Unstructured data and the 80 percent rule. Clarabridge Bridgepoints (2008).
  2. 2.
    Hotho, A., Nürnberger, A., Paaß, G.: A brief survey of text mining. LDV Forum 20(1), 19–62 (2005)Google Scholar
  3. 3.
    OpenCalais: Calais Homepage. March 2013.
  4. 4.
    AlchemyAPI: AlchemyAPI Homepage. March 2013.
  5. 5.
    Seidler, K., Schill, A.: Service-oriented information extraction. In: Proceedings of the Joint EDBT/ICDT Ph.D. Workshop 2011, pp. 25–31 (2011)Google Scholar
  6. 6.
    Evri: Evri Developer Homepage. June 2012.
  7. 7.
    FISE: Furtwangen IKS Semantic Engine project page. March 2013.
  8. 8.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, New York (2007)zbMATHGoogle Scholar
  9. 9.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10, 334–350 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Pfeifer, K., Peukert, E.: Mapping text mining taxonomies. In: KDIR 2013 Proceedings, Scitepress, Portugal (2013)Google Scholar
  11. 11.
    Isaac, A., van der Meij, L., Schlobach, S., Wang, S.: An empirical study of instance-based ontology matching. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 253–266. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  12. 12.
    Massmann, S., Rahm, E.: Evaluating instance-based matching of web directories. In: WebDB 2008 Proceedings (2008)Google Scholar
  13. 13.
    Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: Ontology matching with semantic verification. Web Semant. 7(3), 235–251 (2009)CrossRefGoogle Scholar
  14. 14.
    Do, H.H., Rahm, E.: COMA - a system for flexible combination of schema matching approach. In: VLDB Proceedings (2002)Google Scholar
  15. 15.
    Chua, W.W.K., Kim, J.J.: Discovering cross-ontology subsumption relationships by using ontological annotations on biomedical literature. In: ICBO. CEUR Workshop Proceedings, vol. 897 (2012)Google Scholar
  16. 16.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  17. 17.
    Drumm, C., Schmitt, M., Do, H.H., Rahm, E.: QuickMig: automatic schema matching for data migration projects. In: CIKM’07 Proceedings (2007)Google Scholar
  18. 18.
    Li, J., Tang, J., Li, Y., Luo, Q.: RiMOM: a dynamic multistrategy ontology alignment framework. TKDE 21(8), 1218–1232 (2009)Google Scholar
  19. 19.
    Hu, W., Qu, Y.: Falcon-AO: a practical ontology matching system. Web Semant. 6(3), 237–239 (2008)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Suchanek, F.M., Abiteboul, S., Senellart, P.: Paris: probabilistic alignment of relations, instances, and schema. In: Proceedings of the VLDB Endowment, vol. 5(3), pp. 157–168 (2011)Google Scholar
  21. 21.
    Saleem, K., Bellahsene, Z., Hunt, E.: PORSCHE: performance oriented schema mediation. Inf. Syst. 33, 637–657 (2008)CrossRefGoogle Scholar
  22. 22.
    Raunich, S., Rahm, E.: ATOM: automatic target-driven ontology merging. In: ICDE Proceedings, pp. 1276–1279 (2011)Google Scholar
  23. 23.
    Castano, S., Antonellis, V.D., Vimercati, S.D.C.D., Melchiori, M.: An xml-based integration scheme for web datasources. Ingénierie des Systèmes d’Information 6(1), 99–122 (2001)Google Scholar
  24. 24.
    Dragut, E.C., Wu, W., Sistla, A.P., Yu, C.T., Meng, W.: Merging Source Query Interfaces on Web Databases. In: ICDE Proceedings, vol. 46 (2006)Google Scholar
  25. 25.
    Beneventano, D., Bergamaschi, S., Guerra, F., Vincini, M.: The MOMIS approach to information integration. In: ICEIS 2001 Proceedings, Setubal, Portugal, vol. 1, pp. 194–198 July 2001Google Scholar
  26. 26.
    Pfeifer, K., Meinecke, J.: Identifying the truth - aggregation of named entity extraction results. In: iiWAS’13 Proceedings, ACM, Austria (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.SAP AGDresdenGermany

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