Learning to Rank Knowledge Subgraph Nodes for Entity Retrieval

The importance of entity retrieval, the task of retrieving a ranked list of related entities from big knowledge bases given a textual query, has been widely acknowledged in the literature. In this paper, we propose a novel entity retrieval method that addresses the important challenge that revolves around the need to effectively represent and model context in which entities relate to each other. Based on our proposed method, a model is firstly trained to retrieve and prune a subgraph of a textual knowledge graph that represents contextual relationships between entities. Secondly, a deep model is introduced to reason over the textual content of nodes, edges, and the given question and score and rank entities in the subgraph. We show experimentally that our approach outperforms state-of-the-art methods on a number of benchmarks for entity retrieval.