Knowledge Graph Completion Algorithm Based on Probabilistic Fuzzy Information Aggregation and Natural Language Processing Technology

Abstract

The knowledge graph was first used in the information search of the Internet as a way to improve the quality of the search because it contains a huge amount of structured knowledge data. In this paper, the knowledge map algorithm is studied through natural language processing technology and probabilistic fuzzy information aggregation, and the knowledge map completion algorithm is cognitive-fitted. NLP is natural language processing. Based on the experiments in this paper, it can be seen that, after combining the algorithm, the behavior data set of 1000 Amazon users was analyzed, and it can be found that the accuracy of the algorithm improves as the proportion of data in the experiment increases. Among them, the 10% dataset has a correct rate of 0.66; the 30% dataset has a final accuracy rate of 0.68; and the 50% dataset has a final accuracy rate of 0.70. The experimental results of this paper show that using probabilistic fuzzy information aggregation and natural language processing technology as a way to complete the knowledge graph can improve the accuracy of the operation. It plays an important role in the development of intelligent cognition and search engines.

Keywords

• probabilistic fuzzy sets
• information aggregation
• natural language processing technology
• knowledge graph completion