Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Huda Hakami, Mona Hakami, Angrosh Mandya, Danushka Bollegala


Abstract
Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i.e. with mentions entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, SuperBorrow, that learns to score the suitability of an LDP to represent a without-mentions entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow improves the link prediction performance of multiple widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.
Anthology ID:
2022.naacl-main.209
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2887–2898
Language:
URL:
https://aclanthology.org/2022.naacl-main.209
DOI:
10.18653/v1/2022.naacl-main.209
Bibkey:
Cite (ACL):
Huda Hakami, Mona Hakami, Angrosh Mandya, and Danushka Bollegala. 2022. Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2887–2898, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion (Hakami et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.209.pdf
Code
 huda-hakami/learning-to-borrow-for-kgs
Data
FB15k-237