The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labs

TL;DR

This paper introduces the AnIML Ontology, a formal semantic framework that enhances interoperability of experimental data across diverse scientific labs by aligning AnIML with the Allotrope Data Format.

Contribution

It presents a novel OWL 2 ontology for AnIML, developed with expert input and validated through multiple rigorous testing methods.

Findings

Ontology formalizes AnIML semantics for better interoperability.

Validation confirms the ontology's effectiveness with real-world data.

The approach supports cross-lab and cross-system data integration.

Abstract

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.