Cross-Lingual Type Inference

  • Bo Xu
  • Yi Zhang
  • Jiaqing Liang
  • Yanghua XiaoEmail author
  • Seung-won Hwang
  • Wei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9642)


Entity typing is an essential task for constructing a knowledge base. However, many non-English knowledge bases fail to type their entities due to the absence of a reasonable local hierarchical taxonomy. Since constructing a widely accepted taxonomy is a hard problem, we propose to type these non-English entities with some widely accepted taxonomies in English, such as DBpedia, Yago and Freebase. We define this problem as cross-lingual type inference. In this paper, we present CUTE to type Chinese entities with DBpedia types. First we exploit the cross-lingual entity linking between Chinese and English entities to construct the training data. Then we propose a multi-label hierarchical classification algorithm to type these Chinese entities. Experimental results show the effectiveness and efficiency of our method.


Entity Typing Name Entity Recognition Local Knowledge Base Head Compound English Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Palmero Aprosio, A., Giuliano, C., Lavelli, A.: Automatic expansion of DBpedia exploiting wikipedia cross-language information. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 397–411. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. 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. 722–735. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)Google Scholar
  4. 4.
    Dong, L., Wei, F., Sun, H., Zhou, M., Xu, K.: A hybrid neural model for type classification of entity mentions. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 1243–1249. AAAI Press (2015)Google Scholar
  5. 5.
    Gangemi, A., Nuzzolese, A.G., Presutti, V., Draicchio, F., Musetti, A., Ciancarini, P.: Automatic typing of DBpedia entities. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 65–81. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: a spatially and temporally enhanced knowledge base from wikipedia. Artif. Intell. 194, 28–61 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Lee, T., Wang, Z., Wang, H., Hwang, S.W.: Attribute extraction and scoring: a probabilistic approach. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 194–205. IEEE (2013)Google Scholar
  8. 8.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 5, 1–29 (2014)Google Scholar
  9. 9.
    Ling, X., Weld, D.S.: Fine-grained entity recognition. In: AAAI. Citeseer (2012)Google Scholar
  10. 10.
    Murdock, J.W., Kalyanpur, A., Welty, C., Fan, J., Ferrucci, D.A., Gondek, D., Zhang, L., Kanayama, H.: Typing candidate answers using type coercion. IBM J. Res. Dev. 56(3.4), 7:1–7:13 (2012)Google Scholar
  11. 11.
    Nakashole, N., Tylenda, T., Weikum, G.: Fine-grained semantic typing of emerging entities. In: ACL (1), pp. 1488–1497 (2013)Google Scholar
  12. 12.
    Passant, A.: dbrec — music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Paulheim, H., Bizer, C.: Type inference on noisy RDF data. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 510–525. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Pohl, A.: Classifying the wikipedia articles into the opencyc taxonomy. In: Proceedings of the Web of Linked Entities Workshop in Conjuction with the 11th International Semantic Web Conference, vol. 5, p. 16 (2012)Google Scholar
  16. 16.
    Ponzetto, S.P., Strube, M.: Deriving a large scale taxonomy from wikipedia. In: AAAI, vol. 7, pp. 1440–1445 (2007)Google Scholar
  17. 17.
    Ritter, A., Clark, S., Etzioni, O., et al.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534 (2011)Google Scholar
  18. 18.
    Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22(1–2), 31–72 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Srinivas, S.: A generalization of the noisy-or model. In: Proceedings of the Ninth International Conference on Uncertainty in Artificial Intelligence, pp. 208–215. Morgan Kaufmann Publishers Inc. (1993)Google Scholar
  20. 20.
    Suchanek, F.M., Abiteboul, S., Senellart, P.: Paris: probabilistic alignment of relations, instances, and schema. Proc. VLDB endowment 5(3), 157–168 (2011)CrossRefGoogle Scholar
  21. 21.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)Google Scholar
  22. 22.
    Wang, Z., Li, J., Tang, J.: Boosting cross-lingual knowledge linking via concept annotation. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2733–2739. AAAI Press (2013)Google Scholar
  23. 23.
    Wang, Z., Li, J., Wang, Z., Tang, J.: Cross-lingual knowledge linking across wiki knowledge bases. In: Proceedings of the 21st International Conference on World Wide Web, pp. 459–468. ACM (2012)Google Scholar
  24. 24.
    Yosef, M.A., Bauer, S., Hoffart, J., Spaniol, M., Weikum, G.: Hyena: hierarchical type classification for entity names (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bo Xu
    • 1
  • Yi Zhang
    • 1
  • Jiaqing Liang
    • 1
  • Yanghua Xiao
    • 1
    • 2
    Email author
  • Seung-won Hwang
    • 3
  • Wei Wang
    • 1
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Internet Big Data Engineering and Technology CenterShanghaiChina
  3. 3.Department of Computer ScienceYonsei UniversitySeoulSouth Korea

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