The AI Innovation Taxonomy is a structured vocabulary of 7,490 distinct AI-related concepts, systematically categorized to cover various aspects of Artificial Intelligence. Each concept within the ontology is associated with a unique identifier, preferred labels, alternate labels, broader concepts, and detailed descriptions, facilitating precise semantic annotations and topic categorization. Sample topics include widely recognized areas such as Natural Language Processing, Artificial Intelligence, Machine Translation, Knowledge Representation and Reasoning, Computational Linguistics, Data Mining, Data Science, Text Mining, and Textual Entailment. This ontology provides a robust semantic foundation for accurately annotating, filtering, and categorizing AI-related content, thus supporting consistent and effective topic extraction methodologies. The AI Innovation Taxonomy is developed as part of the research project enrichMyData, specifically the InnoGraph business case that builds a holistic knowledge graph of innovation based on Artificial Intelligence (AI), and more generally of the global “hitech” ecosystem. It has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070284. Publication: The InnoGraph Artificial Intelligence Taxonomy: A Key to Unlocking AI-Related Entities and Content. Alexiev, V.; Bechev, B.; and Osytsin, A. White paper (Technical Report). Ontotext Corp, December 2023.