Description
Ecological synthesis produces knowledge that is human-interpretable but rarely machine-actionable. Attempts to address this through RDF/OWL-based semantic representations introduce a new problem: structures become opaque even to domain experts. We present the Semantic Units Framework, a modular approach to structuring ecological data and knowledge as logic-aware, independently addressable units aligned with the FAIR (Findable, Accessible, Interoperable, Reusable) and CLEAR (Cognitively interoperable, semantically Linked, contextually Explorable, easily Accessible, human-Readable and -interpretable) Principles.
The central contribution is embedding causal knowledge within this architecture. Universal causal statement units encode causal hypotheses as addressable graph entities forming a navigable semantic grid, like a spatial map, to which evidence, publications, and contextual qualifiers can be anchored. Individual observations can be linked to causal hypotheses they support or contradict, enabling queries such as “Which data support this causal claim?” This supports semi-automated causal reasoning, confounder detection, and AI-ready evidence-gap maps.
The framework decomposes knowledge graphs into (a) statement units as single propositions with a persistent identifier, provenance, and method links and (b) compound units grouping related statement units into reusable knowledge objects, both serializable as nanopublication FAIR Digital Objects. Machine-interpretable structures are decoupled from human-facing representations via dynamic labels and graphs, keeping OWL/RDF accessible without specialist knowledge. Currently conceptual with partial prototypes, we invite discussion toward its community-driven realisation.
| Topic | Topic 1: : Methods for integrating social and ecological knowledge |
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