LLM-supported collaborative ontology design for data and knowledge management platforms

TL;DR

This paper introduces a new method for building scientific ontologies using LLMs and domain experts, improving data management and interoperability in materials science.

Contribution

A hybrid human-machine workflow integrating LLMs into ontology design for scientific data platforms.

Findings

The HMIO ontology was successfully integrated into a DKMP, enabling FAIR-compliant data entry.

The approach proved effective for accelerating ontology development while maintaining expert validation.

The methodology is scalable and applicable to other complex scientific domains beyond materials science.

Abstract

The management of vast, heterogeneous, and multidisciplinary data presents a critical challenge across scientific domains, hindering interoperability and slowing scientific progress. This paper addresses this challenge by presenting a pragmatic extension to the NeOn iterative ontology engineering framework, a well-established methodology for collaborative ontology design, which integrates Large Language Models (LLMs) to accelerate key tasks while retaining domain expert-in-the-loop validation. The methodology was applied within the HyWay project, an EU-funded research initiative on hydrogen-materials interactions, to develop the Hydrogen-Material Interaction Ontology (HMIO), a domain-specific ontology covering 29 experimental methods and 14 simulation types for assessing interactions between hydrogen and advanced metallic materials. A key result is the successful integration of the HMIO into a Data and Knowledge Management Platform (DKMP), where it drives the automated generation of data entry forms, ensuring that all captured data is Findable, Accessible, Interoperable, and Reusable (FAIR) and HMIO compliant by design. The validation of this approach demonstrates that this hybrid human-machine workflow for ontology engineering and further integration with the DKMP is an effective and efficient strategy for creating and operationalising complex scientific ontologies, thereby providing a scalable solution to advance data-driven research in materials science and other complex scientific domains.

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