SCISOR: extracting information from on-line news
Communications of the ACM
Information Retrieval
Rapid customization of an information extraction system for a surprise language
ACM Transactions on Asian Language Information Processing (TALIP)
SRI: description of the JV-FASTUS system used for MUC-5
MUC5 '93 Proceedings of the 5th conference on Message understanding
MUC4 '92 Proceedings of the 4th conference on Message understanding
Ontology-based information extraction for business intelligence
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Buy, sell, or hold? information extraction from stock analyst reports
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
A lexico-semantic pattern language for learning ontology instances from text
Web Semantics: Science, Services and Agents on the World Wide Web
Ontology-Based information and event extraction for business intelligence
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Financial events recognition in web news for algorithmic trading
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
MOETA: a novel text-mining model for collecting and analysing competitive intelligence
International Journal of Advanced Media and Communication
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Business intelligence requires the collecting and merging of information from many different sources, both structured and unstructured, in order to analyse for example financial risk, operational risk factors, follow trends and perform credit risk management. While traditional data mining tools make use of numerical data and cannot easily be applied to knowledge extracted from free text, traditional information extraction is either not adapted for the financial domain, or does not address the issue of information integration: the merging of information from different kinds of sources. We describe here the development of a system for content mining using domain ontologies, which enables the extraction of relevant information to be fed into models for analysis of financial and operational risk and other business intelligence applications such as company intelligence, by means of the XBRL standard. The results so far are of extremely high quality, due to the implementation of primarily high-precision rules.