A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling

2025-08-12

AI Summary

Ecological research faces challenges including poor interoperability of heterogeneous data, inconsistent terminology, and difficulties in causal inference. To address these, we propose the Semantic Unit Framework—a modular, logic-aware formalism that models ecological data and knowledge as declarative and composite semantic units, adhering to FAIR (Findable, Accessible, Interoperable, Reusable) and CLEAR (Causal, Linked, Explainable, Auditable, Reproducible) principles. Built on RDF/OWL, the framework implements serializable, persistent-identifier–enabled digital objects with provenance tracking. It achieves semantic alignment with causal modeling formalisms—including do-calculus and Bayesian networks—to support confounder identification and interpretable causal reasoning. Innovatively integrating knowledge graphs with causal modeling, it enables construction of ecological causal graphs and perspective-aware subgraphs, facilitating evidence annotation, AI-ready analysis, and reproducible scientific workflows. This significantly enhances cross-domain knowledge integration and cognitive interoperability.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Ecological research increasingly relies on integrating heterogeneous datasets and knowledge to explain and predict complex phenomena. Yet, differences in data types, terminology, and documentation often hinder interoperability, reuse, and causal understanding. We present the Semantic Units Framework, a novel, domain-agnostic semantic modelling approach applied here to ecological data and knowledge in compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) and CLEAR (Cognitively interoperable, semantically Linked, contextually Explorable, easily Accessible, human-Readable and -interpretable) Principles. The framework models data and knowledge as modular, logic-aware semantic units: single propositions (statement units) or coherent groups of propositions (compound units). Statement units can model measurements, observations, or universal relationships, including causal ones, and link to methods and evidence. Compound units group related statement units into reusable, semantically coherent knowledge objects. Implemented using RDF, OWL, and knowledge graphs, semantic units can be serialized as FAIR Digital Objects with persistent identifiers, provenance, and semantic interoperability. We show how universal statement units build ecological causal networks, which can be composed into causal maps and perspective-specific subnetworks. These support causal reasoning, confounder detection (back-door), effect identification with unobserved confounders (front-door), application of do-calculus, and alignment with Bayesian networks, structural equation models, and structural causal models. By linking fine-grained empirical data to high-level causal reasoning, the Semantic Units Framework provides a foundation for ecological knowledge synthesis, evidence annotation, cross-domain integration, reproducible workflows, and AI-ready ecological research.

Problem

• Enhances interoperability of heterogeneous ecological datasets
• Facilitates causal understanding through semantic modelling
• Supports FAIR and CLEAR compliant knowledge synthesis

Innovation

• Semantic Units Framework for modular data modeling
• RDF and OWL for semantic interoperability
• FAIR Digital Objects with persistent identifiers