Workshop on Research Challenges and Opportunities in Knowledge Representation and Reasoning

Stanford University, Stanford CA

Investigators

Abstract

Modern information systems in every area of science and engineering rely critically on some form of knowledge representation and inference. Established fields such as Artificial Intelligence and Software Engineering as well as emerging fields such as Semantic Web, Computational Biology, Software Agents, Social Computing, Bioinformatics, Discovery Informatics, Cyberlearning and many others both rely on and contribute to advances in knowledge representation. Formal representations of knowledge and the associated inference mechanisms provide the basis for encoding, sharing, analyzing, interpreting vast amounts of data from disparate sources as well as deciding and acting upon information in virtually every area of human endeavor. This workshop aims to bring together scientists from all areas of knowledge-representation research and 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, presents new challenges for knowledge representation (KR). KR researchers must consider the applicability of their methods for representation and reasoning on data and knowledge at unprecedented complexity, quantity, and heterogeneity. The distributed and open nature of the data-intensive science presents additional challenges and opportunities in KR, with regard to provenance, security, trustworthiness, and privacy. The increased adoption of Semantic Web technologies and the rapid increase in the amounts of structured knowledge that is becoming available on the Semantic Web presents additional challenges (e.g., need for coping with information with different degrees of reliability, information that holds in different contexts, information that changes over time, information that can be conflicting, information that represents beliefs and opinions, etc. The increased use of KR methods in Computer Vision, Robotics, Natural-Language Processing, Discovery Informatics, and others presents an opportunity for fruitful interdisciplinary collaborations that could dramatically alter the KR research landscape. As scientists, as well as laypersons get accustomed to social mechanisms for creating and sharing data and knowledge, it is incumbent upon the researchers in knowledge representation to develop KR mechanisms that support collaborative creation, sharing, and use of knowledge. Against this background, the workshop brings together a diverse group of researchers and practitioners in KR and related areas to identify new KR research challenges and opportunities presented by the recent developments in the world wide web, social networks and social media, collaborative and data-intensive e-science, among others. Broader Impacts: The workshop will identify new areas of research for the Information and the Intelligent Systems at the intersection Knowledge Representation and Inference, Information Integration and Informatics. Given the increasingly central role of formal representations of knowledge and associated tools for inference in virtually every domain of human endeavor, the identification of KR the results of the workshop are likely to impact multiple disciplines. The workshop results, including a report summarizing new KR research challenges and opportunities, as well as publications by workshop participants will be broadly disseminated to the larger scientific community.