A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space

Bo Hui, Tian Xia, Wei-Shinn Ku


Abstract
Matching equivalent entities across Knowledge graphs is a pivotal step for knowledge fusion. Previous approaches usually study the problem in Euclidean space. However, recent works have shown that hyperbolic space has a higher capacity than Euclidean space and hyperbolic embedding can represent the hierarchical structure in a knowledge graph. In this paper, we propose a localized geometric method to find equivalent entities in hyperbolic space. Specifically, we use a hyperbolic neural network to encode the lingual information of entities and the structure of both knowledge graphs into a low-dimensional hyperbolic space. To address the asymmetry of structure on different KGs and the localized nature of relations, we learn an instance-specific geometric mapping function based on rotation to match entity pairs. A contrastive loss function is used to train the model. The experiment verifies the power of low-dimensional hyperbolic space for entity matching and shows that our method outperforms the state of the art by a large margin.
Anthology ID:
2022.emnlp-main.182
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2822–2832
Language:
URL:
https://aclanthology.org/2022.emnlp-main.182
DOI:
Bibkey:
Cite (ACL):
Bo Hui, Tian Xia, and Wei-Shinn Ku. 2022. A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2822–2832, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space (Hui et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.182.pdf