Large-Scale Commodity Knowledge Organization and Intelligent Query Optimization

Abstract

The Internet is facing the era of knowledge interconnection Web3.0, and its goal is to realize a more intelligent network that can be understood by both humans and machines. In this environment, various types of knowledge graphs have emerged. Because of the heterogeneity of knowledge, commodity knowledge makes its management more challenging. A large-scale product knowledge organization framework is designed, objective product classification knowledge is combined with subjective user perspectives in the framework, a neural network-based learning index technology is proposed to improve query efficiency. According to the properties of the knowledge structure and the characteristics of query requirements, a connection strategy is realized based on sub-variable combination.The experimental results show that, compared with the existing knowledge management system, the proposed method has a significant improvement in the retrieval efficiency of large-scale commodity knowledge.