CRAFT IBM Research

The CRAFT (Collaborative Reasoning and Analysis Framework and Toolkit) project focuses on supporting the work of a group of people who collect, organize, and reason about information and make decisions. There are many situations in which an organization or group of people must work together to collect information and reach a consensus on what it means. There is often too much information for one person to sift through alone, and the required expertise may be spread among many individuals. We would like to understand how the design of tools can contribute to an environment that encourages people to share their knowledge and perspectives. We want to support a dynamic in which people can easily "bounce ideas off of each other". Applications for this work are numerous, and include business and competitive intelligence, medical diagnosis and epidemiology, intelligence analysis, and strategic forecasting.

Collaborators

Steven I. Ross, Daniel M. Gruen, Susanne C. Hupfer, James E. Christensen, Stephen E. Levy, Jiahui Lui, and John F. Patterson

Technologies

Java and Eclipse RCP

Related Publications

Susanne C. Hupfer, Steven I. Ross, Jamie C. Rasmussen, James E. Christensen, Stephen E. Levy, Daniel M. Gruen, and John F. Patterson. Crafting an environment for collaborative reasoning. In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI). 2009.
Daniel M. Gruen, Jamie C. Rasmussen, Jiahui Lui, Susanne C. Hupfer, and Steven I. Ross. Collaborative reasoning and collaborative ontology development in CRAFT. AAAI Spring Symposium on Semantic Web and Knowledge Engineering (SWKE). 2008.
James E. Christensen, Daniel M. Gruen, Susanne C. Hupfer, Stephen E. Levy, John F. Patterson, Jamie C. Rasmussen, and Steven I. Ross. System and method for ontology-based location of expertise. United States 8,255,380. Issued August 28, 2012.
Jamie C. Rasmussen, Steven I. Ross, Daniel M. Gruen, Susanne C. Hupfer, Stephen E. Levy, James E. Christensen, and John F. Patterson. Program Parameterization through Ontology-Based Models. IBM Research, Technical Report (Prior Art Disclosure). 2009.