OpticE: A Coherence Theory-Based Model for Link Prediction

Xiangyu Gui, Feng Zhao, Langjunqing Jin, Hai Jin


Abstract
Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector or a point in a feature space. The distance between these points is a measure of semantic similarity. However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others. It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities. To alleviate the semantic ambiguity in KGs, we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference. It is a lightweight and relation-adaptive model based on coherence theory, in which each entity’s semantics vary automatically regarding different relations. In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process. The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods.
Anthology ID:
2022.coling-1.433
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4892–4901
Language:
URL:
https://aclanthology.org/2022.coling-1.433
DOI:
Bibkey:
Cite (ACL):
Xiangyu Gui, Feng Zhao, Langjunqing Jin, and Hai Jin. 2022. OpticE: A Coherence Theory-Based Model for Link Prediction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4892–4901, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
OpticE: A Coherence Theory-Based Model for Link Prediction (Gui et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.433.pdf
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FB15k-237