<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2020-mcmh">
    <titleInfo>
        <title>MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Lu</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mo</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tian</namePart>
        <namePart type="family">Gao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.</abstract>
    <identifier type="citekey">zhang-etal-2020-mcmh</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.351</identifier>
    <location>
        <url>https://aclanthology.org/2020.findings-emnlp.351</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>3948</start>
            <end>3954</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
%A Zhang, Lu
%A Yu, Mo
%A Gao, Tian
%A Yu, Yue
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-mcmh
%X Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.
%R 10.18653/v1/2020.findings-emnlp.351
%U https://aclanthology.org/2020.findings-emnlp.351
%U https://doi.org/10.18653/v1/2020.findings-emnlp.351
%P 3948-3954

Markdown (Informal)

[MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning](https://aclanthology.org/2020.findings-emnlp.351) (Zhang et al., Findings 2020)

• MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (Zhang et al., Findings 2020)

ACL

• Lu Zhang, Mo Yu, Tian Gao, and Yue Yu. 2020. MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3948–3954, Online. Association for Computational Linguistics.