Open AccessRETRACTED: Knowledge Verification Method Based on Artificial Intelligence-based Knowledge Graph Construction
Author(s) -
Zhiyao Yang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2146/1/012014
Subject(s) - computer science , knowledge graph , graph , open knowledge base connectivity , knowledge extraction , artificial intelligence , knowledge engineering , knowledge integration , knowledge based systems , knowledge representation and reasoning , procedural knowledge , domain knowledge , machine learning , data mining , theoretical computer science , knowledge management , personal knowledge management , organizational learning
The knowledge graph connects real-world entities and concepts through their relationships, connects all different types of information to obtain a relationship network, and can analyze “relationship” issues. Creating a knowledge graph is a continuous process, and it needs to continuously learn new knowledge and update existing knowledge in the library as time and events change. However, since the accuracy of the updated new knowledge cannot be guaranteed, the new knowledge must be verified. This paper aims to study the knowledge verification method based on artificial intelligence-based knowledge graph construction. Based on the analysis of the knowledge graph construction process, the knowledge graph construction method and the knowledge verification method, knowledge verification is realized by constructing a probabilistic soft logic model. The experimental results show that the recall rate, F1 value, and AUC value of the candidate knowledge set are verified by the knowledge verification model proposed in this paper. Therefore, it can be inferred that the knowledge verification model proposed in this paper is effective.