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Answer-enhanced Path-aware Relation Detection over Knowledge Base

Published: 18 July 2019 Publication History

Abstract

Knowledge Based Question Answering (KBQA) is one of the most promising approaches to provide suitable answers for the queries posted by users. Relation detection that aims to take full advantage of the substantial knowledge contained in knowledge base (KB) becomes increasingly important. Significant progress has been made in performing relation detection over KB. However, recent deep neural networks that achieve the state of the art on KB-based relation detection task only consider the context information of question sentences rather than the relatedness between question and answer candidates, and exclusively extract the relation from KB triple rather than learn informative relational path. In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection. A Siamese LSTM is employed into a similarity matching process between the question representation and relation representation. Experimental results on the SimpleQuestions and WebQSP datasets demonstrate that KRD outperforms the state-of-the-art methods. In addition, a series of ablation test show the robust superiority of the proposed method.

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Cited By

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  • (2024)A Novel Joint Training Model for Knowledge Base Question AnsweringIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.333652632(666-679)Online publication date: 1-Jan-2024
  • (2023)BT-CKBQA: An efficient approach for Chinese knowledge base question answeringData & Knowledge Engineering10.1016/j.datak.2023.102204147(102204)Online publication date: Sep-2023
  • (2023)Meta-path reasoning of knowledge graph for commonsense question answeringFrontiers of Computer Science10.1007/s11704-022-2336-618:1Online publication date: 12-Aug-2023
  • Show More Cited By

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  1. Answer-enhanced Path-aware Relation Detection over Knowledge Base

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    cover image ACM Conferences
    SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2019
    1512 pages
    ISBN:9781450361729
    DOI:10.1145/3331184
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 18 July 2019

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    Author Tags

    1. knowledge base
    2. relation detection
    3. relational path inference
    4. representation learning

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    SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2024)A Novel Joint Training Model for Knowledge Base Question AnsweringIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.333652632(666-679)Online publication date: 1-Jan-2024
    • (2023)BT-CKBQA: An efficient approach for Chinese knowledge base question answeringData & Knowledge Engineering10.1016/j.datak.2023.102204147(102204)Online publication date: Sep-2023
    • (2023)Meta-path reasoning of knowledge graph for commonsense question answeringFrontiers of Computer Science10.1007/s11704-022-2336-618:1Online publication date: 12-Aug-2023
    • (2022)Question Answer System: A State-of-Art Representation of Quantitative and Qualitative AnalysisBig Data and Cognitive Computing10.3390/bdcc60401096:4(109)Online publication date: 7-Oct-2022

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