Multiple Models for Recommending Temporal Aspects of Entities

  • Tu Ngoc NguyenEmail author
  • Nattiya KanhabuaEmail author
  • Wolfgang NejdlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)


Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.



This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under project GlycoRec (16SV7172).


  1. 1.
    Balog, K., Dalton, J., Doucet, A., Ibrahim, Y.: Report on esair’15. In: ACM SIGIR ForumGoogle Scholar
  2. 2.
    Bar-Yossef, Z., Kraus, N.: Context-sensitive query auto-completion. In: WWW 2011 (2011)Google Scholar
  3. 3.
    Bian, J., Li, X., Li, F., Zheng, Z., Zha, H.: Ranking specialization for web search: a divide-and-conquer approach by using topical ranksvm. In: WWW 2010 (2010)Google Scholar
  4. 4.
    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). Scholar
  5. 5.
    Chirigati, F., Liu, J., Korn, F., Wu, Y.W., Yu, C., Zhang, H.: Knowledge exploration using tables on the web. In: Proceedings of the VLDB Endowment (2016)CrossRefGoogle Scholar
  6. 6.
    Deng, H., King, I., Lyu, M.R.: Entropy-biased models for query representation on the click graph. In: Proceedings of SIGIR 2009 (2009)Google Scholar
  7. 7.
    Dessi, A., Atzori, M.: A machine-learning approach to ranking RDF properties. Future Gener. Comput. Syst. 54, 366–377 (2016)CrossRefGoogle Scholar
  8. 8.
    Dou, Z., Song, R., Wen, J.-R.: A large-scale evaluation and analysis of personalized search strategies. In: Proceedings of WWW 2007 (2007)Google Scholar
  9. 9.
    Fischer, L., Blanco, R., Mika, P., Bernstein, A.: Timely semantics: a study of a stream-based ranking system for entity relationships. In: Arenas, M., Corcho, O., Simperl, E., Strohmaier, M., d’Aquin, M., Srinivas, K., Groth, P., Dumontier, M., Heflin, J., Thirunarayan, K., Staab, S. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 429–445. Springer, Cham (2015). Scholar
  10. 10.
    Hasibi, F., Balog, K., Bratsberg, S.E.: Dynamic factual summaries for entity cards. In: SIGIR 2017 (2017)Google Scholar
  11. 11.
    Heitz, G., Gould, S., Saxena, A., Koller, D.: Cascaded classification models: combining models for holistic scene understanding. In: NIPS (2009)Google Scholar
  12. 12.
    Joachims, T.: Training linear svms in linear time. In: Proceedings of KDD 2006 (2006)Google Scholar
  13. 13.
    Kairam, S.R., Morris, M.R., Teevan, J., Liebling, D.J., Dumais, S.T.: Towards supporting search over trending events with social media. In: ICWSM (2013)Google Scholar
  14. 14.
    Kanhabua, N., Ngoc Nguyen, T., Nejdl, W.: Learning to detect event-related queries for web search. In: WWW 2015 Companion. ACM (2015)Google Scholar
  15. 15.
    Kanhabua, N., Ren, H., Moeslund, T.B.: Learning dynamic classes of events using stacked multilayer perceptron networks. CoRR, abs/1606.07219 (2016)Google Scholar
  16. 16.
    Karmaker Santu, S.K., Li, L., Park, D.H., Chang, Y., Zhai, C.: Modeling the influence of popular trending events on user search behavior. In: WWW 2017 (2017)Google Scholar
  17. 17.
    Kong, W., Li, R., Luo, J., Zhang, A., Chang, Y., Allan, J.: Predicting search intent based onl pre-search context. In: SIGIR 2015 (2015)Google Scholar
  18. 18.
    Kulkarni, A., Teevan, J., Svore, K.M., Dumais, S.T.: Understanding temporal query dynamics. In: Proceedings of WSDM 2011 (2011)Google Scholar
  19. 19.
    Lin, T., Pantel, P., Gamon, M., Kannan, A., Fuxman, A.: Active objects: actions for entity-centric search. In: WWW 2012 (2012)Google Scholar
  20. 20.
    Matsubara, Y., Sakurai, Y., Prakash, B.A., Li, L., Faloutsos, C.: Rise and fall patterns of information diffusion: model and implications. In: Proceedings of KDD. ACM (2012)Google Scholar
  21. 21.
    Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: WWW 2010 (2010)Google Scholar
  22. 22.
    Reinanda, R., Meij, E., de Rijke, M.: Mining, ranking and recommending entity aspects. In: Proceedings of SIGIR, pp. 263–272. ACM (2015)Google Scholar
  23. 23.
    Shokouhi, M.: Detecting seasonal queries by time-series analysis. In: SIGIR 2011 (2011)Google Scholar
  24. 24.
    Shokouhi, M., Radinsky, K.: Time-sensitive query auto-completion. In: IGIR 2012 (2012)Google Scholar
  25. 25.
    Silvestri, F.: Mining query logs: turning search usage data into knowledge. Found. Trends Inf. Retrieval 4(1–2), 1–174 (2010)CrossRefGoogle Scholar
  26. 26.
    Spina, D., Meij, E., De Rijke, M., Oghina, A., Bui, M.T., Breuss, M.: Identifying entity aspects in microblog posts. In: SIGIR 2012 (2012)Google Scholar
  27. 27.
    Tran, N.K., Tran, T., Niederée, C.: Beyond time: dynamic context-aware entity recommendation. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 353–368. Springer, Cham (2017). Scholar
  28. 28.
    Vadrevu, S., Tu, Y., Salvetti, F.: Ranking relevant attributes of entity in structured knowledge base. US Patent 9,229,988, 5 Jan 2016Google Scholar
  29. 29.
    Whiting, S., Jose, J.M.: Recent and robust query auto-completion. In: WWW 2014 (2014)Google Scholar
  30. 30.
    Yu, X., Ma, H., Hsu, B.-J.P., Han, J.: On building entity recommender systems using user click log and freebase knowledge. In: Proceedings of WSDM, pp. 263–272. ACM (2014)Google Scholar
  31. 31.
    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). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.L3S Research Center/Leibniz Universität HannoverHannoverGermany
  2. 2.NTENT EspañaBarcelonaSpain

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