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

Canlin Zhang and Kai Lu ()
Additional contact information
Canlin Zhang: Sorenson Communications, Salt Lake City, UT 84123, USA
Kai Lu: Department of Public Safety Technology, Hainan Vocational College of Political Science and Law, Haikou 571100, China

Mathematics, 2022, vol. 10, issue 23, 1-13

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4578/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4578/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:23:p:4578-:d:992060

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().