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This paper introduces the AnIML Ontology, an OWL 2 ontology designed to formalize the semantics of the Analytical Information Markup Language (AnIML) and enhance data interoperability across scientific labs. The ontology development employed an expert-in-the-loop approach, integrating LLM-assisted requirement elicitation with collaborative ontology engineering. Validation involved transforming AnIML files into knowledge graphs, verifying competency questions via SPARQL, and using adversarial negative competency questions mapped to ontological anti-patterns enforced via SHACL constraints.
LLMs can help elicit requirements for ontologies, but adversarial validation is crucial to ensure the resulting knowledge graphs avoid common semantic pitfalls.
Achieving semantic interoperability across heterogeneous experimental data systems remains a major barrier to data-driven scientific discovery. The Analytical Information Markup Language (AnIML), a flexible XML-based standard for analytical chemistry and biology, is increasingly used in industrial R&D labs for managing and exchanging experimental data. However, the expressivity of the XML schema permits divergent interpretations across stakeholders, introducing inconsistencies that undermine the interoperability the AnIML schema was designed to support. In this paper, we present the AnIML Ontology, an OWL 2 ontology that formalises the semantics of AnIML and aligns it with the Allotrope Data Format to support future cross-system and cross-lab interoperability. The ontology was developed using an expert-in-the-loop approach combining LLM-assisted requirement elicitation with collaborative ontology engineering. We validate the ontology through a multi-layered approach: data-driven transformation of real-world AnIML files into knowledge graphs, competency question verification via SPARQL, and a novel validation protocol based on adversarial negative competency questions mapped to established ontological anti-patterns and enforced via SHACL constraints.