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Session Type: Technical-Applied-Research
Session Level: Intermediate-Advanced
SHER: A Scalable Highly Expressive Reasoner
Software Research Group
IBM Thomas J. Watson Research Center
Hawthorne, New York
An impediment to the realization of the semantic web vision has been the inability of semantic web systems to successfully scale to large and expressive knowledge bases. In this talk, I will present advances in scaling ontology reasoning made over the past three years at IBM Research. I will also discuss three concrete applications of scalable ontology reasoning that illustrate its value and potential to transform how information is integrated, analyzed and exchanged. The first application showcases the use of ontology reasoning to automate common labor intensive and error prone clinical tasks, such as cohort selection of patients for clinical trials, infectious disease monitoring, and clinical decision support. An obstacle to automating these tasks is the need to bridge the semantic gap between raw patient data, such as laboratory tests, and the way clinicians interpret this data. The second demonstrates how ontologies can be effectively used to cleanse the output of text analytics over large text corpora. Finally, the last application shows how SHER technology enables efficient semantic search over biomedical literature.
Achille Fokoue (https://domino.researc... (https://domino.research.ibm.com/comm/research_people.nsf/pages/achille.index.html)) is a researcher at IBM T.J. Watson Research Center, NY, USA. His research interests include knowledge representation and reasoning, data management, information integration, and concrete applications of semantic technologies in an enterprise context. His current work focuses on developing theories, algorithms and systems for scaling reasoning over large and very expressive description logics knowledge bases. He currently serves as IBM representative at the W3C OWL 2.0 Working Group.
Introduction to Semantic Web related efforts at NYU
Panagiotis G. Ipeirotis
Information Systems Group
Leonard N. Stern School of Business
New York University
Panos Ipeirotis is an Assistant Professor at the Department of Information, Operations, and Management Sciences at Leonard N. Stern School of Business of New York University. His area of expertise is databases and information retrieval, with an emphasis on management of textual data. His research interests include web searching, text and web mining, data cleaning and data integration. He received his Ph.D. degree in Computer Science from Columbia University in 2004 and a B.Sc. degree from the Computer Engineering and Informatics Department (CEID) of the University of Patras, Greece in 1999. He has received two Microsoft Live Labs Awards, two "Best Paper" awards (IEEE ICDE 2005, ACM SIGMOD 2006), two "Best Paper Runner Up" awards (JCDL 2002, ACM KDD 2008), and is also a recipient of a CAREER award from the National Science Foundation.