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Beyond Time: Dynamic Context-Aware Entity Recommendation

  • Nam Khanh TranEmail author
  • Tuan Tran
  • Claudia Niederée
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)

Abstract

Entities and their relatedness are useful information in various tasks such as entity disambiguation, entity recommendation or search. In many cases, entity relatedness is highly affected by dynamic contexts, which can be reflected in the outcome of different applications. However, the role of context is largely unexplored in existing entity relatedness measures. In this paper, we introduce the notion of contextual entity relatedness, and show its usefulness in the new yet important problem of context-aware entity recommendation. We propose a novel method of computing the contextual relatedness with integrated time and topic models. By exploiting an entity graph and enriching it with an entity embedding method, we show that our proposed relatedness can effectively recommend entities, taking contexts into account. We conduct large-scale experiments on a real-world data set, and the results show considerable improvements of our solution over the states of the art.

Keywords

Contextual entity relatedness Entity recommendation 

Notes

Acknowledgments

This work was partially funded by the German Federal Ministry of Education and Research (BMBF) for the project eLabour (01UG1512C).

References

  1. 1.
    Bi, B., Ma, H., Hsu, B.J.P., Chu, W., Wang, K., Cho, J.: Learning to recommend related entities to search users. In: WSDM (2015)Google Scholar
  2. 2.
    Blanco, R., Cambazoglu, B.B., Mika, P., Torzec, N.: Entity recommendations in web search. 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. LNCS, vol. 8219, pp. 33–48. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41338-4_3CrossRefGoogle Scholar
  3. 3.
    Blanco, R., Ottaviano, G., Meij, E.: Fast and space-efficient entity linking in queries. In: WSDM (2015)Google 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 (2008)Google Scholar
  5. 5.
    Fetahu, B., Markert, K., Anand, A.: Automated news suggestions for populating wikipedia entity pages. In: CIKM (2015)Google Scholar
  6. 6.
    Hoffart, J., Seufert, S., Nguyen, D.B., Theobald, M., Weikum, G.: KORE: keyphrase overlap relatedness for entity disambiguation. In: CIKM (2012)Google Scholar
  7. 7.
    Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust disambiguation of named entities in text. In: EMNLP (2011)Google Scholar
  8. 8.
    Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: ICML (2015)Google Scholar
  9. 9.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)Google Scholar
  10. 10.
    Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: AAAI (2008)Google Scholar
  11. 11.
    Noraset, T., Bhagavatula, C., Downey, D.: Adding high-precision links to wikipedia. In: EMNLP (2014)Google Scholar
  12. 12.
    Ratkiewicz, J., Flammini, A., Menczer, F.: Traffic in social media i: paths through information networks. In: SocialCom (2010)Google Scholar
  13. 13.
    Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using wikipedia. In: AAAI (2006)Google Scholar
  14. 14.
    Tran, N.K., Ceroni, A., Kanhabua, N., Niederée, C.: Supporting interpretations of forgotten stories by time-aware re-contextualization. In: WSDM (2015)Google Scholar
  15. 15.
    Tran, T.A., Niederee, C., Kanhabua, N., Gadiraju, U., Anand, A.: Balancing novelty and salience: adaptive learning to rank entities for timeline summarization of high-impact events. In: CIKM (2015)Google Scholar
  16. 16.
    Tuan, T.A., Elbassuoni, S., Preda, N., Weikum, G.: CATE: context-aware timeline for entity illustration. In: WWW (2011)Google Scholar
  17. 17.
    Wang, Y., Zhu, M., Qu, L., Spaniol, M., Weikum, G.: Timely YAGO: harvesting, querying, and visualizing temporal knowledge from wikipedia. In: EDBT (2010)Google Scholar
  18. 18.
    Wulczyn, E., Taraborelli, D.: Wikipedia clickstream (2015)Google Scholar
  19. 19.
    Yu, X., Ma, H., Hsu, B.J.P., Han, J.: On building entity recommender systems using user click log and freebase knowledge. In: WSDM (2014)Google Scholar
  20. 20.
    Zhang, L., Rettinger, A., Zhang, J.: A probabilistic model for time-aware entity recommendation. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 598–614. Springer, Cham (2016). doi: 10.1007/978-3-319-46523-4_36CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.L3S Research CenterLeibniz Universität HannoverHanoverGermany

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