An automated information extraction system from the knowledge graph based annual financial reports

• Published in: PeerJ. Computer science Vol. 10; p. e2004
• Main Authors: Mohsin, Syed Farhan, Jami, Syed Imran, Wasi, Shaukat, Siddiqui, Muhammad Shoaib
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 13.05.2024; PeerJ Inc
• Subjects: Annual financial reports; Artificial Intelligence; Banking industry; Emerging Technologies; Financial institutions; Forecasts and trends; Information extraction; Knowledge graph; Ontology; Questions and answers; Semantic web; World Wide Web and Web Science; Pakistan; Semantic web; Information extraction; Ontology; Annual financial reports; Artificial intelligence; Knowledge graph
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2004

This article presents a semantic web-based solution for extracting the relevant information automatically from the annual financial reports of the banks/financial institutions and presenting this information in a queryable form through a knowledge graph. The information in these reports is significantly desired by various stakeholders for making key investment decisions. However, this information is available in an unstructured format making it much more complex and challenging to understand and query manually or even through digital systems. Another challenge that makes the understanding of information more complex is the variation of terminologies among financial reports of different banks or financial institutions. The solution presented in this article signifies an ontological approach to solving the standardization problems of the terminologies in this domain. It further addresses the issue of semantic differences to extract relevant data sharing common semantics. Such semantics are then incorporated by implementing their representation as a Knowledge Graph to make the information understandable and queryable. Our results highlight the usage of Knowledge Graph in search engines, recommender systems and question-answering (Q-A) systems. This financial knowledge graph can also be used to serve the task of financial storytelling. The proposed solution is implemented and tested on the datasets of various banks and the results are presented through answers to competency questions evaluated on precision and recall measures.