AI is becoming pervasive and possibly subtle, sometimes thoroughly embedded in architectures, powering and controlling technical systems at all levels, and, for this reason, it is relevant not only to the technical community, but also to all classes of users and to the public in general.
Technical advances, as well as the increasing complexity of AI-powered technologies, exceed the ability of individuals and public sector organizations to understand and regulate their operation and deployment, leading to potentially inestimable risks.
Knowledge representation (KR) was introduced decades ago, in the early days of AI, by scholars such as MeDermott [1], Bobrow and Winograd [2] and others. KR consists of techniques, constructs and artefacts required by computers to handle information expressed as concepts using natural language. Although primarily intended as a computational technique, KR can also be useful to codify and support explicit and functional human knowledge exchange, and understanding and learning about a subject.

This Special Issue tackles AI KR as a possible path to increasing the understanding, communication, and knowledge of advances in artificial intelligence for both humans and machines. It aims to break new ground in addressing KR in relation to a vast range of AI-related innovations emerging from R&D. Therefore, the scope and impact of this Special Issue is potentially very high.