Modern intelligent systems in every area of science rely critically on knowledge representation and reasoning (KR). The techniques and methods developed by the researchers in knowledge representation and reasoning are key drivers of innovation in computer science; 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 the semantic web, computational biology, social computing, and many others rely on and contribute to advances in knowledge representation. As the era of “Big Data” evolves, scientists in a broad range of disciplines are increasingly relying on knowledge representation to analyze, aggregate, and process the vast amounts of data and knowledge that today’s computational methods generate.
We convened the Workshop on Research Challenges and Opportunities of Knowledge Representation in order to take stock in the past decade of KR research, to analyze where the major challenges are, to identify new opportunities where novel KR research can have major impact, and to determine how we can improve KR education as part of the core Computer Science curriculum. The main outcome of the workshop is a set of recommendations both for KR research and for policy-‐ makers to enable major advancements that will have broad impact in science, technology, and education.
de Kleer, J.,
Patel-Schneider, P. F.,
& Schildhauer, M.
(2013). Research Challenges and Opportunities in Knowledge Representation. .