A pragmatist approach to bridging tables and ontologies through LinkML and punning.

Abstract

Background: Ontologies support data integration and knowledge discovery across multiple life science disciplines through the application of formal semantics that define hierarchical and cross-hierarchical relationships which enable automated reasoning capabilities. At the same time, tabular data is by far the most commonly produced format of data in most disciplines. This is especially true in biodiversity dataset production. Creating rich semantic data from tables can be complex because of a mismatch between flat tabular data and hierarchical ontology models. Here, we present a practical approach for closing this gap using Linked Data Modeling Language (LinkML) in combination with OWL punning. LinkML allows tabular schemas to be formally defined, validated, and automatically documented. OWL punning allows schema elements to function simultaneously as both classes and data properties. This approach has many practical advantages that we showcase using mammal trait data assembled from the Ranges Digitization Network.

Results: Our work demonstrates how using LinkML + punning simplifies alignment of trait data in tabular format with ontologies such as the FuTRES Ontology of Vertebrate Traits (FOVT). Additionally, we show multiple advantages of our practical approach, including: maintaining synchronization between schema, ontology, and documentation, reducing modeling overhead, and preserving compatibility with conventional relational data workflows. On the downside, full logical reasoning is limited relative to more verbose RDF translations. Often this is a perfectly acceptable tradeoff for many use cases focused on data discovery and integration.

Conclusion: The LinkML + punning framework shown here provides a scalable and pragmatic strategy for building semantically rich, ontology-aligned data resources that lower the barrier to semantic interoperability in biodiversity and trait informatics.