About the workshop

   

Dates: February 7-8, 2013 (Thursday and Friday)

Location: Westin Arlington Gateway 

                 (Scott Fitzgerald A meeting room)


Modern intelligent systems in every area of science rely critically on knowledge representation and reasoning. The techniques and methodologies developed by the researchers in knowledge representation and reasoning are key drivers of innovation in computer science, and they have led to significant advances in practical applications in a wide range of areas from natural-language processing to robotics to software engineering. Emerging fields such as semantic web, computational biology, software agents, social computing, and many others rely on and contribute to advances in knowledge representation. Scientists in many disciplines that are supported by the National Science Foundation, from environmental sciences, to social science, to life sciences, to basic sciences, are beginning to rely on knowledge representation to analyze, aggregate, and process the vast amounts of data and knowledge that today’s computational methods generate. 

This workshop will bring together scientists from all areas of knowledge-representation research to discuss the new challenges and opportunities that this area faces in addressing the explosion of data and knowledge, increased reliance of scientists on computational data, its heterogeneity, and new modes of delivering, storing, and representing knowledge. This radical shift in the amount of data, in the way that scientists distribute, store, and aggregate this data, precipitates new challenges for knowledge representation (KR). KR researchers must address scalability of their methods for representation and reasoning on entirely different scale. The distributed and open nature of the data-intensive science requires representation and reasoning about provenance, security, and privacy. The increased adoption of semantic web technologies and the rapid increase in the amounts of structured knowledge that is represented on the semantic web creates its own set of challenges. The increased use of KR methods in computer vision, robotics, and natural-language processing emphasizes the opportunity for practitioners in those fields to affect directions in which KR research proceeds. As scientists and the rest of the Web users, get more accustomed to social mechanisms for creating and sharing data, it is incumbent upon the researchers in knowledge representation to study how these new interaction and knowledge creation paradigms affect the field.

To summarize, we believe that the following recent advances and changes in the landscape of the applications of KR technologies make this workshop particularly timely and unique in its scope: 
  • increased reliance of scientists on knowledge representation methods to “tame” the big data explosion in a distributed and open world 
  • increased availability of linked open data and the need to develop principled methods to represent and use this data in applications 
  • increased need for background knowledge in processing images, video, and natural-language text and for integration tasks

The objective of the workshop will be to analyze the new research challenges that the dramatic shifts in data-intensive science bring to the fore. The workshop will focus on interactive brainstorming sessions with the goal of producing a report that outlines the key challenges and opportunities for knowledge representation and reasoning.

Workshop Co-Chairs
  • Natasha Noy, Stanford University
  • Deborah McGuinness, Rensselaer Polytechnic Institute
  • Eyal Amir, University of Illinois, Urbana-Champaign
NSF Program Manager
  • Vasant Honavar, National Science Foundation
Final workshop report

    Contact: noy@stanford.edu