Ontology-based Information Extraction for Business Intelligence

A paper written by Horacio Saggion, Diana Maynard, Kalina Bontcheva and Adam Funk. It was presented at the ISWC2007+ASWC2007.

[edit] Abstract

Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of bulding a complete end-to-end solution.

A linked list of all papers is provided in the article on ISWC2007+ASWC2007 papers. This article has originally been created from the ISWC 2007/ASWC 2007 metadata.