Message Passing for Hyper-Relational Knowledge Graphs

Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann


Abstract
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.
Anthology ID:
2020.emnlp-main.596
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7346–7359
Language:
URL:
https://aclanthology.org/2020.emnlp-main.596
DOI:
10.18653/v1/2020.emnlp-main.596
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.596.pdf
Optional supplementary material:
 2020.emnlp-main.596.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939108