ISWC 2016: The Semantic Web – ISWC 2016 pp 598-614 | Cite as

A Probabilistic Model for Time-Aware Entity Recommendation

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)

Abstract

In recent years, there has been an increasing effort to develop techniques for related entity recommendation, where the task is to retrieve a ranked list of related entities given a keyword query. Another trend in the area of information retrieval (IR) is to take temporal aspects of a given query into account when assessing the relevance of documents. However, while this has become an established functionality in document search engines, the significance of time has not yet been recognized for entity recommendation. In this paper, we address this gap by introducing the task of time-aware entity recommendation. We propose the first probabilistic model that takes time-awareness into consideration for entity recommendation by leveraging heterogeneous knowledge of entities extracted from different data sources publicly available on the Web. We extensively evaluate the proposed approach and our experimental results show considerable improvements compared to time-agnostic entity recommendation approaches.

Keywords

Dynamic Relatedness Keyword Query Related Entity Page View Anchor Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 611346.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute AIFBKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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