Related entity finding (REF) is a promising application, which aims to return a list of related entities given a query that describes the source entity, the specific type of target entities, and the relation between the source entity and target entities. One typical entity ranking strategy is to rank the candidate entities based on the co-occurrence between the candidate entities and the given query. However, such a strategy is inadequate to rank entities properly especially for those related but unpopular entities. In this paper, we propose a bipartite graph based entity ranking method, which leverage the Co-List relationship between candidate entities (i.e., entities co-occurring in the same structured/unstructured lists) to help improve the entity ranking. Specifically, we first estimate the initial relevance scores for the candidate entities based on a generative probabilistic model. We then construct a bipartite graph based on Co-List relation between candidate entities, and apply an iterative refinement process analogous to heat diffusion on the graph to propagate the relevance scores over entities. Finally the candidate entities are ranked according to their refined scores. We further develop an optimization framework for the iterative refinement process in our ranking method. Experimental results on the data collection from the TREC 2010 Entity Track show the effectiveness of our proposed method.
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