Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction

Adrian Kochsiek, Apoorv Saxena, Inderjeet Nair, Rainer Gemulla

Abstract
We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP model that exploits textual features of the KG, has small model size, and is scalable. To reach good predictive performance, however, KGT5 relies on an ensemble with a knowledge graph embedding model, which itself is excessively large and costly to use. In this short paper, we show empirically that adding contextual information — i.e., information about the direct neighborhood of the query entity — alleviates the need for a separate KGE model to obtain good performance. The resulting KGT5-context model is simple, reduces model size significantly, and obtains state-of-the-art performance in our experimental study.
Anthology ID:
2023.repl4nlp-1.11
Volume:
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Lena Voita
Venue:
RepL4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–138
Language:
URL:
https://aclanthology.org/2023.repl4nlp-1.11
DOI:
10.18653/v1/2023.repl4nlp-1.11
Bibkey:
Cite (ACL):
Adrian Kochsiek, Apoorv Saxena, Inderjeet Nair, and Rainer Gemulla. 2023. Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction. In Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023), pages 131–138, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction (Kochsiek et al., RepL4NLP 2023)
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https://preview.aclanthology.org/naacl24-info/2023.repl4nlp-1.11.pdf
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