Advertisement

Entity Linking in Web Tables with Multiple Linked Knowledge Bases

  • Tianxing WuEmail author
  • Shengjia Yan
  • Zhixin Piao
  • Liang Xu
  • Ruiming Wang
  • Guilin Qi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10055)

Abstract

The World-Wide Web contains a large scale of valuable relational data, which are embedded in HTML tables (i.e. Web tables). To extract machine-readable knowledge from Web tables, some work tries to annotate the contents of Web tables as RDF triples. One critical step of the annotation is entity linking (EL), which aims to map the string mentions in table cells to their referent entities in a knowledge base (KB). In this paper, we present a new approach for EL in Web tables. Different from previous work, the proposed approach replaces a single KB with multiple linked KBs as the sources of entities to improve the quality of EL. In our approach, we first apply a general graph-based algorithm to EL in Web tables with each single KB. Then, we leverage the existing and newly learned “sameAs” relations between the entities from different KBs to help improve the results of EL in the first step. We conduct experiments on the sampled Web tables with Zhishi.me, which consists of three linked encyclopedic KBs. The experimental results show that our approach outperforms the state-of-the-art table’s EL methods in different evaluation metrics.

Keywords

Entity linking Web tables Linked knowledge bases 

Notes

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 61272378, the 863 Program under Grant No. 2015AA015406 and the Research Innovation Program for College Graduates of Jiangsu Province under Grant No. KYLX16_0295.

References

  1. 1.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_52 CrossRefGoogle Scholar
  2. 2.
    Bhagavatula, C.S., Noraset, T., Downey, D.: TabEL: entity linking in web tables. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 425–441. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25007-6_25 CrossRefGoogle Scholar
  3. 3.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. Web Seman. Sci. Serv. Agents WWW 7(3), 154–165 (2009)CrossRefGoogle Scholar
  4. 4.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)Google Scholar
  5. 5.
    Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)CrossRefGoogle Scholar
  6. 6.
    Cafarella, M.J., Halevy, A., Wang, D.Z., Wu, E., Zhang, Y.: Webtables: exploring the power of tables on the web. PVLDB 1(1), 538–549 (2008)Google Scholar
  7. 7.
    Craswell, N.: Mean reciprocal rank. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, p. 1703. Springer, Heidelberg (2009)Google Scholar
  8. 8.
    Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Hignette, G., Buche, P., Dibie-Barthélemy, J., Haemmerlé, O.: Fuzzy annotation of web data tables driven by a domain ontology. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 638–653. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02121-3_47 CrossRefGoogle Scholar
  10. 10.
    Limaye, G., Sarawagi, S., Chakrabarti, S.: Annotating and searching web tables using entities, types and relationships. PVLDB 3(1–2), 1338–1347 (2010)Google Scholar
  11. 11.
    Mulwad, V., Finin, T., Joshi, A.: Semantic message passing for generating linked data from tables. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 363–378. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41335-3_23 CrossRefGoogle Scholar
  12. 12.
    Muñoz, E., Hogan, A., Mileo, A.: Using linked data to mine RDF from wikipedia’s tables. In: WSDM, pp. 533–542 (2014)Google Scholar
  13. 13.
    Navigli, R., Ponzetto, S.P.: Babelnet: building a very large multilingual semantic network. In: ACL, pp. 216–225 (2010)Google Scholar
  14. 14.
    Niu, X., Sun, X., Wang, H., Rong, S., Qi, G., Yu, Y.: Zhishi.me - weaving chinese linking open data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7032, pp. 205–220. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25093-4_14 CrossRefGoogle Scholar
  15. 15.
    Pereira, B.: Entity linking with multiple knowledge bases: an ontology modularization approach. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 513–520. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11915-1_33 Google Scholar
  16. 16.
    Shen, W., Wang, J., Luo, P., Wang, M.: Liege: link entities in web lists with knowledge base. In: SIGKDD, pp. 1424–1432 (2012)Google Scholar
  17. 17.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW, pp. 697–706 (2007)Google Scholar
  18. 18.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a large ontology from wikipedia and wordnet. Web Seman. Sci. Serv. Agents WWW 6(3), 203–217 (2008)CrossRefGoogle Scholar
  19. 19.
    Syed, Z., Finin, T., Mulwad, V., Joshi, A.: Exploiting a web of semantic data for interpreting tables. In: WebSci, vol. 5 (2010)Google Scholar
  20. 20.
    Venetis, P., Halevy, A., Madhavan, J., Paşca, M., Shen, W., Wu, F., Miao, G., Wu, C.: Recovering semantics of tables on the web. PVLDB 4(9), 528–538 (2011)Google Scholar
  21. 21.
    Zhang, Z.: Learning with partial data for semantic table interpretation. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 607–618. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-13704-9_45 Google Scholar
  22. 22.
    Zhang, Z.: Towards efficient and effective semantic table interpretation. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 487–502. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11964-9_31 Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Tianxing Wu
    • 1
    Email author
  • Shengjia Yan
    • 1
  • Zhixin Piao
    • 1
  • Liang Xu
    • 1
  • Ruiming Wang
    • 1
  • Guilin Qi
    • 1
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations