Message Passing for Hyper-Relational Knowledge Graphs

Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - StarE capable of modeling such hyper-relational KGs....

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Bibliographic Details
Main Authors Galkin, Mikhail, Trivedi, Priyansh, Maheshwari, Gaurav, Usbeck, Ricardo, Lehmann, Jens
Format Journal Article
LanguageEnglish
Published 22.09.2020
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computation and Language
Computer Science - Learning
Statistics - Machine Learning
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Summary:Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - StarE capable of modeling such hyper-relational KGs. Unlike existing approaches, StarE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that StarE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.
DOI:10.48550/arxiv.2009.10847