Knowledge Representation and Extraction for Business Intelligence

author: Horacio Saggion, Departament of Information and Communication Technologies, Pompeu Fabra University
author: Thierry Declerck, German Research Center for Artificial Intelligence (DFKI)
published: Nov. 24, 2008,   recorded: October 2008,   views: 5981

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Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide business analysts with valuable information or feed statistical BI models and tools. The massive amount of textual and multimedia information available to business analysts makes information extraction and semantic-based digital tools key enablers for the acquisition and management of semantic information. The role of Ontologies is important here, since they promote interoperability and uniform and standardized access to heterogeneous sources and software components. In addition they encode rules for deduction of new knowledge from extracted data.

The tutorial will give an overview of approaches to identify, extract, and consolidate semantic information for business intelligence, also stressing the role of temporal information. The tutorial will take a practical hands-on approach in which theoretical concepts and approaches are presented together with case studies on semantic-based tools in the context of the 6th Framework Programme Musing Integrated Project which is targeting three different vertical domains: Financial Risk Management; Internationalisation; and IT Operational Risk Management.

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