A probability model for related entity retrieval using relation pattern

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.