Abstract
The fine-grained target categories/types are very critical for improving the performance of entity search because they can be used for retrieving relevant entities by filtering irrelevant entities with a high confidence. However, most solutions of entity search face an urgent problem, i.e., the lack of fine-grained target categories of queries, which are hard for users to explicitly specify. In this paper, we try to interpret fine-grained categories from natural language based queries of entity search. We observe that entity search queries often contain terms specifying the contexts of the desired entities, as well as a topic of the desired entities. Accordingly, we propose to interpret fine-grained categories of entity search queries from the context perspective and the topic perspective. Therefore, we propose an approach by formalizing both context-based category model and topic-based category model, to tackle the category interpreting task. Extensive experiments on two widely-used test sets: INEX-XER 2009 and SemSearch-LS, indicate significant performance improvement achieved by our proposed method over the state-of-the-art baselines.
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References
Chen, Y., Gao, L., Shi, S., Du, X., Wen, J.: Improving context and category matching for entity search. In: AAAI, pp. 16–22 (2014)
Balog, K., Bron, M., Rijke, R.: Query modeling for entity search based on terms, categories, and examples. ACM Trans. Inf. Syst. 29(4), 22 (2011)
de Vries, A.P., Vercoustre, A.-M., Thom, J.A., Craswell, N., Lalmas, M.: Overview of the INEX 2007 entity ranking track. In: Fuhr, N., Kamps, J., Lalmas, M., Trotman, A. (eds.) INEX 2007. LNCS, vol. 4862, pp. 245–251. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85902-4_22
Demartini, G., de Vries, A.P., Iofciu, T., Zhu, J.: Overview of the INEX 2008 entity ranking track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2008. LNCS, vol. 5631, pp. 243–252. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03761-0_25
Demartini, G., Iofciu, T., de Vries, A.P.: Overview of the INEX 2009 entity ranking track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2009. LNCS, vol. 6203, pp. 254–264. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14556-8_26
Balog, K., Rijke, M.: Combining Candidate and Document Models for Expert Search. TREC (2008)
Vercoustre, A.-M., Pehcevski, J., Thom, J.A.: Using Wikipedia categories and links in entity ranking. In: Fuhr, N., Kamps, J., Lalmas, M., Trotman, A. (eds.) INEX 2007. LNCS, vol. 4862, pp. 321–335. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85902-4_28
Kaptein, R., Kamps, J.: Exploiting the category structure of Wikipedia for entity ranking. Artif. Intell. 194, 111–129 (2013)
Garigliotti, D., Balog, K.: On type-aware entity retrieval. In: ICTIR, pp. 27–34 (2017)
Balog, K., Neumayer, R.: Hierarchical target type identification for entity-oriented queries. In: CIKM, pp. 2391–2394 (2012)
Garigliotti, D., Hasibi, F., Balog, K.: Target type identification for entity-bearing queries. In: SIGIR, pp. 845–848 (2017)
Wang, Z., Wang, H., Hu, Z.: Head, modifier, and constraint detection in short texts. In: ICDE, pp. 280–291 (2014)
Bendersky, M., Metzler, D., Croft, W.: Learning concept importance using a weighted dependence model. In: WSDM, pp. 31–40 (2010)
Balog, K., Neumayer, R.: A test collection for entity search in DBpedia. In: SIGIR, pp. 737–740 (2013)
Roi, B., Harry, H., Daniel, M., Peter, M., Jeffrey, P., David, R., Henry, T.: Entity search evaluation over structured web data. In: SIGIR (2011)
Liang, S., Rijke, M.: Formal language models for finding groups of experts. Inf. Process. Manag. 52(4), 529–549 (2016)
Macdonald, C., Ounis, I.: Voting techniques for expert search. Knowl. Inf. Syst. 16(3), 259–280 (2008)
Metzler, D., Bruce, W.: Linear feature-based models for information retrieval. Inf. Retr. 10(3), 257–274 (2007)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimationand model selection. In: IJCAI, pp. 1137–1145 (1995)
Wu, W., Li, H., Wang, H., Zhu, K.: Probase: a probabilistic taxonomy for text understanding. In: SIGMOD, pp. 481–492 (2012)
Acknowledgments
Yueguo Chen is supported by the National Science Foundation of China under grants No. U1711261, 61472426, 61432006, and the State Visiting Scholar Funds from the China Scholarship Council under Grant Number 201706365018. Denghao Ma is supported by the Outstanding Innovative Talents Cultivation Funded Programs 2017 of Renmin University of China and the State Scholarship Fund from China Scholarship Council under Grant Number 201706360309.
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Ma, D., Chen, Y., Du, X., Hao, Y. (2018). Interpreting Fine-Grained Categories from Natural Language Queries of Entity Search. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_55
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