Abstract
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.
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Maynard, D., Saggion, H., Yankova, M., Bontcheva, K., Peters, W. (2007). Natural Language Technology for Information Integration in Business Intelligence. In: Abramowicz, W. (eds) Business Information Systems. BIS 2007. Lecture Notes in Computer Science, vol 4439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72035-5_28
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DOI: https://doi.org/10.1007/978-3-540-72035-5_28
Publisher Name: Springer, Berlin, Heidelberg
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