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        <title>Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval</title>
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        <namePart type="given">Zhenghao</namePart>
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        <namePart type="given">Maosong</namePart>
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    <name type="personal">
        <namePart type="given">Zhiyuan</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1223</identifier>
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%0 Conference Proceedings
%T Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
%A Liu, Zhenghao
%A Xiong, Chenyan
%A Sun, Maosong
%A Liu, Zhiyuan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F liu-etal-2018-entity
%X 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.
%R 10.18653/v1/P18-1223
%U https://aclanthology.org/P18-1223
%U https://doi.org/10.18653/v1/P18-1223
%P 2395-2405

Markdown (Informal)

[Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval](https://aclanthology.org/P18-1223) (Liu et al., ACL 2018)

• Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (Liu et al., ACL 2018)

ACL

• Zhenghao Liu, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. 2018. Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2395–2405, Melbourne, Australia. Association for Computational Linguistics.