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A Relation Pattern-Driven Probability Model for Related Entity Retrieval

A Relation Pattern-Driven Probability Model for Related Entity Retrieval

Peng Jiang, Qing Yang, Chunxia Zhang, Zhendong Niu, Hongping Fu
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 14
ISSN: 1947-8208|EISSN: 1947-8216|EISBN13: 9781466613171|DOI: 10.4018/jkss.2012010105
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MLA

Jiang, Peng, et al. "A Relation Pattern-Driven Probability Model for Related Entity Retrieval." IJKSS vol.3, no.1 2012: pp.64-77. http://doi.org/10.4018/jkss.2012010105

APA

Jiang, P., Yang, Q., Zhang, C., Niu, Z., & Fu, H. (2012). A Relation Pattern-Driven Probability Model for Related Entity Retrieval. International Journal of Knowledge and Systems Science (IJKSS), 3(1), 64-77. http://doi.org/10.4018/jkss.2012010105

Chicago

Jiang, Peng, et al. "A Relation Pattern-Driven Probability Model for Related Entity Retrieval," International Journal of Knowledge and Systems Science (IJKSS) 3, no.1: 64-77. http://doi.org/10.4018/jkss.2012010105

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Abstract

As the Web is becoming the largest knowledge repository which contains various entities and their relations, the task of related entity retrieval excites interest in the field of information retrieval. This challenging task is introduced in TREC 2009 Entity Track. In this task, given an entity and the type of the target entity, a retrieval system is required to return a ranked list of related entities extracted from a given large corpus. It means that entity ranking goes beyond entity relevance and integrates the judgment of relation into the evaluation of the retrieved entities. This paper proposes a probability model using relation patterns to address the task of related entity retrieval. This model takes into account both relevance and relation between entities. The authors focus on using relation patterns to measure the level of relations matching between entities, and then to estimate the probability of occurrence of relation between two entities. In addition, the authors represent entity by its context language model and measure the relevance between two entities by a language model. Experimental results on TREC Entity Track dataset show that the proposed model significantly improves retrieval performances over baseline. The comparison with other approaches also reveals the effectiveness of the model.

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