SUKE: Embedding Model for Prediction in Uncertain Knowledge Graph

Wang, Jingbin; Nie, Kuan; Chen, Xinyuan; Lei, Jing

doi:10.1109/access.2020.3047086

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…Although uncertainty information is preserved in the embedding space, the model does not consider the correlation information in the knowledge graph. Both UKGE [46] and SUKE [47] use the scoring function of the Distmult model and map the scoring function to confidence through a function. They both optimize using a mean squared error loss function.…”

Section: Uncertain Knowledge Graph Representation Learning Modelsmentioning

confidence: 99%

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Shen,

2024

Mathematics

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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.

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Section: Uncertain Knowledge Graph Representation Learning Modelsmentioning

confidence: 99%