Learning contextual representations for entity retrieval

In this paper, we introduce Contextual Entity Ranking (CoER) for the task of entity retrieval. CoER utilizes a textual knowledge graph to learn entities’ representations that are contextualized based on a given query. With these contextual representations and the query, CoER includes a set of models that learn to rank relevant entities. The introduced ranking models measure semantic relevance between entities’ contextual representations and the textual query, between entities’ contextual representations along with entities’ non-contextual and general descriptions and the textual query, and finally, between entities’ contextual representations and their relevance to the entities in the given query. We empirically illustrate that CoER is effective in retrieving and ranking entities across different benchmark datasets compared with state-of-the-art models. We also report ablation studies that investigate the impact of the contextual representation model and the ranking models on the final performance.