关键词: 知识图谱, 链接预测, 贝叶斯网, 相似性, 概率推理

Abstract: Link prediction is to discover and recover missing information in a knowledge graph (KG). Combining external knowledge and employing some specified methods to fulfill link prediction is the topic with great attention and key problem in KG research. Taking e-commerce application as the background, this paper combines the KG that has been constructed to describe user interest with external data, and adopts Bayesian network (BN), an important probabilistic graphical model, as the framework for representing and inferring the similarities among commodities as well as corresponding uncertainties. This paper constructs the BN to reflect the similarities by statistic computations upon commodity properties, and evaluates the authenticity of the links between commodity and user nodes based on the probabilistic inference mechanism of BN. Consequently, the real and complete KG can be obtained, as the basis of personalized recommendation and correlation query processing. The experimental results established on real data show the effectiveness of the model and algorithms proposed in this paper.

Key words: knowledge graph, link prediction, Bayesian network, similarity, probabilistic inference