Enhancing Trait Thesauri Interoperability Using a Manual and Automated Alignment Approach

Over the past decade, trait data collection and mobilisation have expanded significantly, yet much of this data remains only partially compliant with FAIR principles. A major challenge lies in the inconsistent use of standards for harmonising heterogeneous trait data, along with the diversity, redundancy, and poor alignment of semantic artefacts developed to address this challenge. This study presents an approach to enhance the interoperability of the Trait Thesauri developed within the LifeWatch Italy research infrastructure for annotating and standardising trait data and metadata of aquatic organisms. This approach combines manual and automated alignment techniques, tested within the 2023 Ontology Alignment Evaluation Initiative. Domain experts manually aligned the Phytoplankton, Zooplankton, Macroalgae, Macrozoobenthos, and Fish trait thesauri, while five matching tools, LogMap, LogMapKG, LogMapLt, Matcha, and OLaLa, were tested for automated mappings. Both approaches revealed significant overlap among thesauri: Manual mapping identified 160 cross-thesauri correspondences and served as a benchmark for evaluating automated matching systems. Automated tools showed variable performance, with OLaLa achieving the best automated alignment results, with an F-measure of 0.93. Challenges in alignment included varying linguistic expressions and differing levels of concept specificity. The results highlight the importance of combining automation with expert validation to ensure mapping quality and allowed the development of a unified Trait Thesaurus, which integrates approximately 500 harmonised concepts, reducing redundancy and enhancing FAIR compliance in ecological and trait-based research.