Curbing Crime with Data Extraction & 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.
A strong development leader, Greg has more than 15 years of experience doing enterprise-scale .NET architecture and development for leading companies such as Convercent, Fujitsu, Lehman Brothers and SAP. He has deep expertise in SaaS, Web 3.0, ontology design, service-oriented architecture and the semantic Web. As a manager, Greg exercises process rigor, utilizing Agile development methodologies to deliver software on-time and complete. Outside the development pit, he can be found sharpening his development skills and building robots.
Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 150 published research papers, primarily in the areas of machine learning and natural language processing. He was the President of the International Machine Learning Society from 2008-2011, and program co-chair for the 2006 AAAI Conference on Artificial Intelligence, general chair of the 2005 Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, and co-chair of the 1990 International Conference on Machine Learning. He is a Fellow of both the American Association for Artificial Intelligence and the Association for Computing Machinery, and the recipient of best paper awards from the National Conference on Artificial Intelligence, the SIGKDD International Conference on Knowledge Discovery and Data Mining, the International Conference on Machine Learning, and the Annual Meeting of the Association for Computational Linguistics.