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This paper presents the Entity-Duet Neural Ranking Model (EDRM), whichintroduces knowledge graphs to neural search systems. EDRM represents queriesand documents by their words and entity annotations. The semantics fromknowledge graphs are integrated in the distributed representations of theirentities, while the ranking is conducted by interaction-based neural rankingnetworks. The two components are learned end-to-end, making EDRM a naturalcombination of entity-oriented search and neural information retrieval. Ourexperiments on a commercial search log demonstrate the effectiveness of EDRM.Our analyses reveal that knowledge graph semantics significantly improve thegeneralization ability of neural ranking models.

Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval