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Using data crawlers and semantic Web to build financial XBRL data generators: the SONAR extension approach.
Miguel Ángel Rodríguez-García1, Alejandro Rodríguez-González2, Ricardo Colomo-Palacios3
1Department of Informatics and Systems, University of Murcia, Espinardo, 30100 Murcia, Spain.
Thescientificworldjournal
|March 4, 2014
Summary
This study introduces the SONAR approach to transform scattered financial data into structured knowledge using semantic technologies. It enhances financial services by providing reliable, real-time information for market recovery.
Area of Science:
- Financial Informatics
- Semantic Web Technologies
- Knowledge Representation
Background:
- Economic turmoil necessitates reliable, real-time financial data for enhanced services.
- The Web is a primary data source, requiring intelligent extraction methods.
- Semantic technologies, NLP, and ontology engineering are key for knowledge discovery.
Purpose of the Study:
- To present the SONAR extension approach for financial knowledge extraction and management.
- To convert scarce and dispersed financial information into structured, XBRL format-oriented knowledge.
- To evaluate the benefits of the SONAR approach through a proof-of-concept implementation.
Main Methods:
- Utilizing intelligent crawlers based on Semantic Technologies.
- Combining Natural Language Processing (NLP) and ontology engineering techniques.
- Developing a proof-of-concept implementation for the SONAR approach.
Main Results:
- Demonstrated extraction and management of financial information.
- Successful conversion of data into well-classified, structured XBRL format.
- Evaluation confirmed the benefits of the SONAR approach for financial knowledge representation.
Conclusions:
- The SONAR approach effectively leverages knowledge representation for financial data.
- It provides a robust method for transforming raw web data into valuable financial knowledge.
- This facilitates improved financial services and market recovery.
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