<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kochsiek-gemulla-2023-benchmark">
    <titleInfo>
        <title>A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Adrian</namePart>
        <namePart type="family">Kochsiek</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rainer</namePart>
        <namePart type="family">Gemulla</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle, such an approach is infeasible for large-scale KGs, where retraining is expensive and new entities may arise frequently. In this paper, we propose and describe a large-scale benchmark to evaluate semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It provides transductive, k-shot, and 0-shot LP tasks, each varying the available information from (i) only KG structure, to (ii) including textual mentions, and (iii) detailed descriptions of the entities. We report on a small study of recent approaches and found that semi-inductive LP performance is far from transductive performance on long-tail entities throughout all experiments. The benchmark provides a test bed for further research into integrating context and textual information in semi-inductive LP models.</abstract>
    <identifier type="citekey">kochsiek-gemulla-2023-benchmark</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.713</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.713</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>10634</start>
            <end>10643</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs
%A Kochsiek, Adrian
%A Gemulla, Rainer
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kochsiek-gemulla-2023-benchmark
%X Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle, such an approach is infeasible for large-scale KGs, where retraining is expensive and new entities may arise frequently. In this paper, we propose and describe a large-scale benchmark to evaluate semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It provides transductive, k-shot, and 0-shot LP tasks, each varying the available information from (i) only KG structure, to (ii) including textual mentions, and (iii) detailed descriptions of the entities. We report on a small study of recent approaches and found that semi-inductive LP performance is far from transductive performance on long-tail entities throughout all experiments. The benchmark provides a test bed for further research into integrating context and textual information in semi-inductive LP models.
%R 10.18653/v1/2023.findings-emnlp.713
%U https://aclanthology.org/2023.findings-emnlp.713
%U https://doi.org/10.18653/v1/2023.findings-emnlp.713
%P 10634-10643

Markdown (Informal)

[A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs](https://aclanthology.org/2023.findings-emnlp.713) (Kochsiek & Gemulla, Findings 2023)

• A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs (Kochsiek & Gemulla, Findings 2023)

ACL

• Adrian Kochsiek and Rainer Gemulla. 2023. A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10634–10643, Singapore. Association for Computational Linguistics.