CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Autor: Qiuhui Shen, Aiyan Qu
Jazyk: angličtina
Rok vydání: 2024
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Zdroj: Mathematics, Vol 12, Iss 10, p 1419 (2024)
Druh dokumentu: article
ISSN: 12101419
2227-7390
DOI: 10.3390/math12101419
Popis: 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.
Databáze: Directory of Open Access Journals
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