An Uncertain Knowledge Graph Prediction Method Based on Improved Embedding Model Suke

Abstract

The present invention relates to an uncertain knowledge graph prediction method based on an improved embedding model SUKE. The SUKE model is proposed based on the existing deterministic embedding model DistMult. SUKE retains the structural information and uncertainty information of knowledge. It includes two components: an evaluator and a confidence generator. The former evaluates the rationality of facts based on the structural characteristics and uncertain characteristics of facts, and screens out unreasonable facts. to obtain candidate facts. The latter generates confidences for candidate facts, representing the probability that a particular relationship occurs with an entity. The evaluator defines a structure score and an uncertainty score for each triplet for the factual plausibility assessment task. In addition, the estimator introduces unknown facts to participate in the training. The confidence generator generates a confidence score for each triplet, which is used in the confidence prediction task. The invention can effectively complete the link prediction task of the uncertain knowledge graph.

Description

technical field [0001] The invention relates to the technical field of knowledge representation and reasoning under the knowledge graph, in particular to an uncertain knowledge graph prediction method based on an improved embedded model SUKE. Background technique [0002] The uncertain knowledge graph provides a confidence score for each triple, and the confidence reflects the probability of the occurrence of the triple. In recent years, the development of relation extraction and crowdsourcing has promoted the construction of large-scale uncertain knowledge graphs, such as: ConceptNet, Probase, and NELL, etc. URGE was proposed by Hu et al. in 2017. URGE proposes a matrix factorization-based method to embed uncertain networks. But this model only considers node proximity in sparse networks and only learns node embeddings. UKGE, proposed by Chen et al. in 2019, utilizes the confidence of triples to learn embeddings, while introducing probabilistic soft logic to infer unseen...

AI Technical Summary

Problems solved by technology

For the design of uncertain networks, the URGE model considers the proximity of nodes to generate node embeddings. Although the URGE model can be extended to knowledge graphs, there are differences between uncertain networks and knowledge graphs, and it cannot complete uncertain knowledge graphs well. embedded task

Compared with URGE, UKGE has better performance, but the UKGE model does not make full use of the structural information of knowledge to a certain extent

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