Abstract

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.

Date

March 20, 2026

Authors

Natasha Noy, Deborah McGuinness, Eyal Amir, Chitta Baral, Michael Beetz, Sean Bechhofer, Craig Boutilier, Anthony Cohn, Johan de Kleer, Michel Dumontier, Tim Finin, Kenneth Forbus, Lise Getoor, Yolanda Gil, Jeff Heflin, Pascal Hitzler, Craig Knoblock, Henry Kautz, Yuliya Lierler, Vladimir Lifschitz, Peter F Patel-Schneider, Christine Piatko, Doug Riecken, Mark Schildhauer