Entity Query Feature Expansion Using Knowledge Base Links

Dalton, J. , Dietz, L. and Allan, J. (2014) Entity Query Feature Expansion Using Knowledge Base Links. In: 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Gold Coast, Qld, Australia, 6-11 Jul 2014, pp. 365-374. ISBN 9781450322577 (doi: 10.1145/2600428.2609628)

Abstract

Recent advances in automatic entity linking and knowledge base construction have resulted in entity annotations for document and query collections. For example, annotations of entities from large general purpose knowledge bases, such as Freebase and the Google Knowledge Graph. Understanding how to leverage these entity annotations of text to improve ad hoc document retrieval is an open research area. Query expansion is a commonly used technique to improve retrieval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text. We experiment using both explicit query entity annotations and latent entities. We evaluate our technique on TREC text collections automatically annotated with knowledge base entity links, including the Google Freebase Annotations (FACC1) data. We find that entity-based feature expansion results in significant improvements in retrieval effectiveness over state-of-the-art text expansion approaches.

• Item Type: Conference Proceedings
• Keywords: Entities, information extraction, information retrieval, ontologies.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dalton, Dr Jeff
• Authors: Dalton, J., Dietz, L., and Allan, J.
• Subjects: T Technology > T Technology (General)
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: ACM
• ISBN: 9781450322577
• Copyright Holders: Copyright © 2014 The Authors
• First Published: First published in Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval: 365-374
• Publisher Policy: Reproduced in accordance with the publisher copyright policy