实体-下神经神经分级:了解知识图义学在神经信息检索中的作用 (Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval)

This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.

翻译：本文介绍实体-企业神经分级模型(EDRM),该模型向神经搜索系统介绍知识图表,EDRM通过文字和实体说明代表查询和文件,知识图表的语义被纳入各实体的分布式表述中,而排名则由基于互动的神经分级网络进行,这两个组成部分都是从尾到尾的学习,使EDRM成为面向实体的搜索和神经信息检索的自然组合。我们在商业搜索日志上的实验证明了EDRM的有效性。我们的分析表明,知识图表的语义学极大地提高了神经分级模型的通用能力。