IEPAD: information extraction based on pattern discovery
Proceedings of the 10th international conference on World Wide Web
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A CDD-based formal model for expert finding
Proceedings of the sixteenth ACM conference on 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
EntityRank: searching entities directly and holistically
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Concordance-Based Entity-Oriented Search
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Combining document- and paragraph-based entity ranking
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
SystemT: a system for declarative information extraction
ACM SIGMOD Record
Using Wikipedia to bootstrap open information extraction
ACM SIGMOD Record
A supervised learning approach to entity search
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Category-based query modeling for entity search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
A pattern learning approach to question answering within the ephyra framework
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
A Relation Pattern-Driven Probability Model for Related Entity Retrieval
International Journal of Knowledge and Systems Science
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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, as well as the nature of their relation described in free text, a retrieval system is required to return a ranked list of related entities that are of the target type. It means that entity ranking goes beyond entity relevance and integrates the judgment of relation into the evaluation of the retrieved entities. In this paper, we propose a probability model using relation pattern to address the task of related entity retrieval. This model takes into account both relevance and relation between entities. We focus on using relation patterns to measure the level of relation matching between entities, and then to estimate the probability of occurrence of relation between two entities. In addition, we represent entity by its context language model and measure the relevance between two entities by a language model approach. Experimental results on TREC Entity Track dataset show that our proposed model significantly improves retrieval performances over baseline. The comparison with other approaches also reveals the effectiveness of our model.