ISWC-tutorial-slides

ISWC-tutorial-slides - KnowledgeRepresentationand...

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1 Knowledge Representation and  Extraction for Business  Intelligence Thierry Declerck (DFKI), Horacio  Saggion (University of Sheffield),  Marcus Spies (STI University of  Innsbruck) 
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2 Notes Contributors Christian Leibold Hans-Ulrich Krieger Bernd Kiefer Slides and updates at: http://www.gate.ac.uk/conferences/iswc08- tutorial
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3 Main Objectives of the MUSING  Project Creation of the next generation of industrial analysis: the  semantic-based Business Intelligence; Development and validation of BI solutions with emphasis on Credit  Risk Management (Basel II and beyond); Development and validation of semantic-based internationalisation  platforms; Development and validation of semantic-driven knowledge systems  for IT-OpR measurement and mitigation tools, with particular  reference to operational risks/business continuity issues faced by  IT-intensive  organisations; Validation of the research and technological development  results in those domains with high societal impact. Exploitation  of the multi-industry potential.
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4 Main Research and Development  objectives   Representation of temporal information  European Internationalisation policies  (Bayesian) integration of qualitative and quantitative knowledge  elements Integration of the various scientific communities involved in  MUSING Contributions to standards
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5 General overview of semantic  technologies in MUSING
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6 MUSING Ontologies
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7 Data Sources in MUSING Data sources are provided by MUSING partners and  include balance sheets, company profiles, press data,  web data, etc. (some private data) Il Sole 24 ORE, CreditReform data Companies’ web pages (main, “about us”, “contact us”, etc.) Wikipedia, CIA Fact Book, etc. Ontology is manually developed through interaction  with domain experts and ontology curators It extends the PROTON ontology and covers the financial,  international, and IT operative risk domain
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8 Processing Structured and  Unstructured Date Ontology-driven analysis of both structured and unstructured textual  data Structured Data normalized:  extracting from the  tables the data (terms, values, dates, currency, etc.) and map them into a  normalized representation in XBRL, the eXtensible Business Reporting  Language. Company Profiles and International Reports, which give detailled information 
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This note was uploaded on 11/15/2011 for the course I SYS 201 taught by Professor Meservy during the Winter '11 term at BYU.

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ISWC-tutorial-slides - KnowledgeRepresentationand...

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