  This paper explores entity embedding effectiveness in ad-hoc entity
retrieval, which introduces distributed representation of entities into entity
retrieval. The knowledge graph contains lots of knowledge and models entity
semantic relations with the well-formed structural representation. Entity
embedding learns lots of semantic information from the knowledge graph and
represents entities with a low-dimensional representation, which provides an
opportunity to establish interactions between query related entities and
candidate entities for entity retrieval. Our experiments demonstrate the
effectiveness of entity embedding based model, which achieves more than 5\%
improvement than the previous state-of-the-art learning to rank based entity
retrieval model. Our further analysis reveals that the entity semantic match
feature effective, especially for the scenario which needs more semantic
understanding.
