关键词: 电子商务, 知识图谱, 关联查询, 贝叶斯网, Spark计算引擎

Abstract: To model the correlations among entities in knowledge graph (KG) effectively,by taking electronic commerce application as the background and Bayesian network (BN) as the framework of knowledge representation and inference,an effective method was proposed to fuse the domain knowledge in KG and the knowledge implied in users' historical behavior records.Consequently,the BN that described the uncertain correlation among commodities was constructed,and the indirect correlations among commodities were computed based on BN inferences.The proposed method provided underlying techniques for correlation query processing of KG,and solution to classical e-commerce applications such as product classification,user targeting and recommendation.With respect to the large-scale KG and massive users' historical behavior records,the Spark-based parallel algorithms were given for model construction and probabilistic inferences.Experimental results on real data showed that the proposed method could be used to discover the associations that were not directly described in KGs with the recall close to 90%,and fulfilled the correlation query processing efficiently on the KG with more than 100 million edges.

Key words: e-commerce, knowledge graph, correlation query, Bayesian network, Spark compute engine