<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ye-etal-2022-generative">
    <titleInfo>
        <title>Generative Knowledge Graph Construction: A Review</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hongbin</namePart>
        <namePart type="family">Ye</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ningyu</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hui</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Huajun</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.</abstract>
    <identifier type="citekey">ye-etal-2022-generative</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.1</identifier>
    <location>
        <url>https://aclanthology.org/2022.emnlp-main.1</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>1</start>
            <end>17</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generative Knowledge Graph Construction: A Review
%A Ye, Hongbin
%A Zhang, Ningyu
%A Chen, Hui
%A Chen, Huajun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ye-etal-2022-generative
%X Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
%R 10.18653/v1/2022.emnlp-main.1
%U https://aclanthology.org/2022.emnlp-main.1
%U https://doi.org/10.18653/v1/2022.emnlp-main.1
%P 1-17

Markdown (Informal)

[Generative Knowledge Graph Construction: A Review](https://aclanthology.org/2022.emnlp-main.1) (Ye et al., EMNLP 2022)

• Generative Knowledge Graph Construction: A Review (Ye et al., EMNLP 2022)

ACL

• Hongbin Ye, Ningyu Zhang, Hui Chen, and Huajun Chen. 2022. Generative Knowledge Graph Construction: A Review. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1–17, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.