Metadata and Vocabulary for Knowledge Representation Learning

Abstract

Artificial Intelligence (AI) is advancing rapidly, introducing both opportunities and risks. A critical gap exists in the explicit use of Knowledge Representation (KR) within AI standards and practice. This paper presents an initial, alphabetically sorted vocabulary of terms for KRL (Knowledge Representation Learning), justifies the approach, evaluates outcomes, and sets the stage for future refinement in the context of vocabulary standardization for AI. The work aims to bridge semantic gaps, enhance explainability, and support trustworthy AI by standardizing the terminology to be used of AI resource description. This work is presented to the metadata and vocabulary research community to foster discussions and collaboration.