Abstract
The primary method used by the banking industry to disseminate its financial figures is annual financial reports. These intricate and extensive reports require manual processing to extract meaningful information, which causes delays and uncertainty in investment decisions. It is challenging to create an automated system for answering intelligent financial enquiries. A number of studies have been suggested to use information extraction to solve these issues, however they do not address the semantic interoperability of the reports across various organizations. In order to respond to financial queries utilizing ontology-based information extraction, this work presented an automated querying engine. A Financial Knowledge Graph has been proposed to aid in the semantic modelling of financial reporting. Our project seeks to shorten the amount of time users spend learning about the stakes that shareholders and promoters have in a company. Instead of having to manually search through thousands of files, someone can now quickly find out all the details about a corporation. By employing a simple search query, the user will be able to see not just the most recent trends in the organization’s various shareholding patterns, but also historical trends, saving a tonne of time. The final objective is to offer a comprehensive online directory (Knowledge Graph) that will, in a graphical and understandable way, respond to any question the user may have regarding the shareholdings of various organizations and people within a company.
Keywords
Ontology-based Information Extraction, Web Scrapping, Knowledge Graph, Machine Learning
Citations
IRE Journals:
Diptesh Ravindra Varule , Shweth Goverdhan Shetty , Rushikesh Sandeep Borse , Prof. Naman V. Buradkar
"Know About Company" Iconic Research And Engineering Journals Volume 6 Issue 8 2023 Page 146-150
IEEE:
Diptesh Ravindra Varule , Shweth Goverdhan Shetty , Rushikesh Sandeep Borse , Prof. Naman V. Buradkar
"Know About Company" Iconic Research And Engineering Journals, 6(8)
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