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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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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