We are very pleased to announce that our paper “Message Passing for Hyper-Relational Knowledge Graphs” was accepted for presentation at EMNLP2020 (Empirical Methods in Natural Language Processing).
EMNLP is a leading conference in the area of Natural Language Processing. EMNLP invites the submission of long and short papers on substantial, original, and unpublished research in empirical methods for Natural Language Processing.
Here is the pre-print of the accepted paper with its abstract:
- Message Passing for Hyper-Relational Knowledge Graphs
By Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, and Jens Lehmann.
AbstractHyper-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.
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