arXiv subjects

Tim Alamenciak

Publications and source records attributed to Tim Alamenciak.

2 recordsLinked to original sources

arXiv2026

The TripleA Principle: Making Knowledge Actionable, Applicable, and Auditable in Post-FAIR Infrastructures

Ecological restoration, species distribution modelling, and invasive species management share a difficulty: knowledge that is findable and reusable carries no explicit account of the conditions under which it can be validly applied, or of the evidence grounding them. Applying it correctly is therefore demanding and expert-dependent, and misapplication usually goes unrecorded. The FAIR and CLEAR principles improved the findability, accessibility, interoperability, reusability, and human-interpretability of knowledge, but these address properties of representation, and reliable action requires more. Bridging the knowledge-action gap requires characterizing knowledge in terms of the operations it supports. Analysing what an operation needs, we derive three capabilities a knowledge representation must support. Actionability is the capacity to supply the knowledge and objective an operation executes. Applicability is the capacity to assess whether it can be reliably performed, through explicit conditions evaluated against context. Auditability is the capacity to assess the empirical grounding for that reliability, through documented success and failure. These form the three criteria of the TripleA Principle, an implementation-indipendent guide for next-generation knowledge infrastructures, jointly sufficient for the representational preconditions of reliably grounded action though not for its justification. Building on the Semantic Units Framework, we realize the principle as action units, typed components in which the knowledge an operation executes, the conditions under which it may validly be applied, and its documented successes and failures are addressable and evaluable. Action units form a nested hierarchy in which documented failure refines the conditions of valid use, letting knowledge graphs act as context-sensitive, evidentially accountable decision-support systems.

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arXiv2025

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

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.

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