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Entity Retrieval via Type Taxonomy Aware Smoothing

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Advances in Information Retrieval (ECIR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10772))

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Abstract

We investigate the task of ad-hoc entity retrieval from a knowledge base. We propose a type taxonomy aware smoothing method that exploits the hierarchical type information of a knowledge base and integrates into an existing language modelling framework. Unlike most existing type-aware retrieval models, our approach does not require an explicit inference of query type. Instead, it directly encodes the type information into a term-based retrieval model by considering the occurrence of query terms in multi-fielded pseudo documents of entities whose types have connections in the type taxonomy. We conduct experiments on a recent public benchmark dataset with the Wikipedia category information. Preliminary experiment results show that our framework improves the performance of existing models.

The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14203414).

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Correspondence to Xinshi Lin .

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Lin, X., Lam, W. (2018). Entity Retrieval via Type Taxonomy Aware Smoothing. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_75

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  • DOI: https://doi.org/10.1007/978-3-319-76941-7_75

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76940-0

  • Online ISBN: 978-3-319-76941-7

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