CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Qiuhui Shen () and Aiyan Qu
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Qiuhui Shen: School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China
Aiyan Qu: College of Network Security, Jinling Institute of Technology, Nanjing 211199, China

Mathematics, 2024, vol. 12, issue 10, 1-19

Abstract: Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information.

Keywords: knowledge graph; uncertain knowledge graph; knowledge representation learning; uncertainty information; confidence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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