Learning Knowledge Graph Embedding in Semantic Space: A Novel Bi-linear Semantic Matching Method

Chen, Kai; Wang, Ye; Li, Aiping; Li, ChenChen; Zhao, Xiaoguang; Ding, Ruidong

doi:10.1109/ijcnn52387.2021.9533477

Cited by 3 publications

(1 citation statement)

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“…Models working on Static Knowledge graph have been well studied (Zhang et al, 2019b;Xu et al, 2020b;Mao et al, 2020;Chen et al, 2021a) with semantic and structure information. Translation based methods, e.g.…”

Section: Related Workmentioning

confidence: 99%

RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion

Chen¹,

Wang²,

Li³

et al. 2022

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts. However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability. In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton's quaternion space. We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can further capture time-evolved relations by theory. Empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.

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Section: Related Workmentioning

confidence: 99%