Efficient string matching: an aid to bibliographic search
Communications of the ACM
The Journal of Machine Learning Research
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Ranking very many typed entities on wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Proceedings of the 2008 ACM symposium on Applied computing
The Computer Journal
Exploiting locality of Wikipedia links in entity ranking
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Selectively diversifying web search results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning models for ranking aggregates
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
A learned approach for ranking news in real-time using the blogosphere
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
Foundations and Trends in Information Retrieval
An exploration of ranking models and feedback method for related entity finding
Information Processing and Management: an International Journal
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Entity search is an emerging research topic in Information Retrieval, where the goal is to rank not documents, but entities in response to a given query. A particularly challenging example of this search scenario is when a user's underlying information need is for a list of entities related to a given entity, represented in the query. In this paper, we propose to tackle this problem as a voting process, by considering the occurrence of an entity among the top ranked documents for a given query as a vote for the existence of a relationship between this and the entity in the query. Our proposed approach is evaluated using a large Web test collection, in the context of the TREC 2009 Entity track. The results attest the effectiveness of our approach when compared to the top participants at TREC, with unparalleled gains in terms of recall. Moreover, through a comprehensive failure analysis, we uncover important issues to be considered when tackling this new search scenario and draw valuable insights towards achieving an effective related entity search performance.