Exploiting External Knowledge and Entity Relationship for Entity Search

Entity search has received abroad attentions and researches that aim to retrieve entities matching the query. Conventional methods focus on entity search task on local dataset, e.g. INEX Wikipedia test collection, where the descriptions of entities are given and relationships between entities are also known. In this paper, we propose an entity search method to handle real-world queries, which need to crawl related descriptions of entities and construct relations between entities manually. By mining historical query records and offline data, our method builds an entity relationship network to model the similarity of entities, and converts the entity search problem to within-network classification problem, which can introduce many novel solutions. Then we use the entity relationship based approach as an offline solution and external knowledge based approach as an online solution to build an ensemble classifier for handling entity search problem. Comprehensive experiments on realworld dataset demonstrate that our method can deal with entity search task effectively and obtain satisfactory performance.