Identification of End-User Economical Relationship Graph Using Lightweight Blockchain-Based BERT Model.

Autor: Jagdish M; Department of Information Technology, Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, India., Shah DU; Department of Electrical Engineering, K. J. Institute of Engineering & Technology, Savli, Vadodara, India., Agarwal V; Center for Management Studies, Jain (Deemed-to-be-University), Bangalore, India., Loganathan GB; Department of Mechatronics, Faculty of Engineering, Tishk International University-Erbil, Kurdistan Region, Iraq., Alqahtani A; Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia., Rahin SA; United International University, Dhaka, Bangladesh.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 May 06; Vol. 2022, pp. 6546913. Date of Electronic Publication: 2022 May 06 (Print Publication: 2022).
DOI: 10.1155/2022/6546913
Abstrakt: Current methods for extracting information from user resumes do not work well with unstructured user resumes in economic announcements, and they do not work well with documents that have the same users in them. Unstructured user information is turned into structured user information templates in this study. It also proposes a way to build person relationship graphs in the field of economics. First, the lightweight blockchain-based BERT model (B-BERT) is trained. The learned B-BERT pretraining model is then utilized to get the event instance vector, categorize it appropriately, and populate the hierarchical user information templates with accurate user characteristics. The aim of this research is that it has investigated the approach of creating character connection graphs in the Chinese financial system and suggests a framework for doing so in the economic sector. Furthermore, the relationship between users is found through the filled-in user information template, and a graph of user relationships is made. This is how it works: finally, the experiment is checked by filling in a manually annotated dataset. In tests, the method can be used to get text information from unstructured economic user resumes and build a relationship map of people in the financial field. The experimental results show that the proposed approach is capable of efficiently retrieving information from unstructured financial personnel resume text and generating a character relationship graph in the economic sphere.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Mukta Jagdish et al.)
Databáze: MEDLINE
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