International Semantic Web Conference

The Semantic Web - ISWC 2015 pp 474-491

Improving Entity Retrieval on Structured Data

Conference paper

DOI: 10.1007/978-3-319-25007-6_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9366)
Cite this paper as:
Fetahu B., Gadiraju U., Dietze S. (2015) Improving Entity Retrieval on Structured Data. In: Arenas M. et al. (eds) The Semantic Web - ISWC 2015. Lecture Notes in Computer Science, vol 9366. Springer, Cham

Abstract

The increasing amount of data on the Web, in particular of Linked Data, has led to a diverse landscape of datasets, which make entity retrieval a challenging task. Explicit cross-dataset links, for instance to indicate co-references or related entities can significantly improve entity retrieval. However, only a small fraction of entities are interlinked through explicit statements. In this paper, we propose a two-fold entity retrieval approach. In a first, offline preprocessing step, we cluster entities based on the x–means and spectral clustering algorithms. In the second step, we propose an optimized retrieval model which takes advantage of our precomputed clusters. For a given set of entities retrieved by the BM25F retrieval approach and a given user query, we further expand the result set with relevant entities by considering features of the queries, entities and the precomputed clusters. Finally, we re-rank the expanded result set with respect to the relevance to the query. We perform a thorough experimental evaluation on the Billions Triple Challenge (BTC12) dataset. The proposed approach shows significant improvements compared to the baseline and state of the art approaches.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.L3S Research CenterLeibniz Universität HannoverHanoverGermany

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