Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources, necessitating substantial collaborative efforts of domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). This method can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies, as well as unstructured textual sources. We assessed DRAGON-AI across ten diverse ontologies, making use of extensive manual evaluation of results. We demonstrate high precision for relationship generation, close to but lower than precision from logic-based reasoning. We also demonstrate definition generation comparable with but lower than human-generated definitions. Notably, expert evaluators were better able to discern subtle flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues. These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

We introduce an extension to the Protégé ontology editor, which allows for discovering concept definitions, which are not explicitly present in axioms, but are logically implied by an ontology. The plugin supports ontologies formulated in the Description Logic EL, which underpins the OWL 2 EL profile of the Web Ontology Language and despite its limited expressiveness captures most of the biomedical ontologies published on the Web. The developed tool allows to verify whether a concept can be defined using a vocabulary of interest specified by a user. In particular, it allows to decide whether some vocabulary items can be omitted in a formulation of a complex concept. The corresponding definitions are presented to the user and are provided with explanations generated by an ontology reasoner.

Editor's Note: An update to this article has been posted here on 7/14/04. As the hype of past decades fades, the current heir to the artificial intelligence legacy may well be ontologies. Evolving from semantic network notions, modern ontologies are proving quite useful. And they are doing so without relying on the jumble of rule-based techniques common in earlier knowledge representation efforts. These structured depictions or models of known (and accepted) facts are being built today to make a number of applications more capable of handling complex and disparate information. They appear most effective when the semantic distinctions that humans take for granted are crucial to the application's purpose.

What is the Semantic Web? "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation" Machines should not just be able to display data, but rather be able to use it for automation, integration and reuse across various applications. The European Commission is funding numerous projects related to Ontologies and the Semantic Web; even more will be funded in its recently launched Sixth Framework Research Programme. The worldwide Semantic Web community is growing rather fast and forces are being joined with other technology developments such as Web Services or multimedia. Last, but not least, vendors are already offering mature products and solutions based on semantic technologies. Thus, the Semantic Web is currently moving from being a vision to becoming reality.