Curbing Crime with Data Extraction and Ontologies
Human activity – good and bad, legal and criminal, ethical and unethical – has become increasingly bound up in data-driven systems. For organizations of all kinds – government, non-profit, businesses – discovering bad behavior when it first occurs, and stopping it in its tracks, is becoming vital to global reputations. In this panel, we’ll look at the practical use of data extraction, ontologies, and the semantic web to detect patterns of misconduct early. We’ll see live examples of how historical and transactional data can be scanned in its native format and language to uncover patterns that provide true predictive analytics and artificial intelligence.
Additional Supporting Materials
- What does a kickback scheme look like?
- What does bribery look like?
- How can artificial intelligence be used to prevent crime?
- What does human behavior have to do with data-driven systems?
- How can ontologies and data extraction be used to predict future human behavior?
- Greg Ebert, Director of Development, Business Controls
- Ray Mooney, Professor of Computer Science, University of Texas at Austin
Megan Kratzman, Director of Marketing, Business Controls
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