Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge

2026-05-11

AI Summary

Current scientific knowledge exists primarily as static assertions, which are difficult to autonomously verify, reconcile when contradictory, or dynamically update, and rely on centralized maintenance—rendering them vulnerable to institutional disruption. This work proposes the first operational framework for autonomous FAIR Digital Objects (aFDOs), endowing knowledge objects with semantic-web-based autonomous decision-making capabilities through a three-layer architecture of policies, announcements, and consensus. The policy layer integrates RDF-star, PROV-O, SHACL, and ODRL; event notifications leverage ActivityStreams 2.0; and a novel Byzantine fault-tolerant consensus mechanism weights participant input by reputation and confidence. Empirical evaluation demonstrates that the framework resolves 56.3% of ClinVar conflicts across 4,305 rare disease ontology FDOs and maintains graceful degradation under adversarial conditions where the fraction of faulty nodes f satisfies f < n/5.

Abstract

Scientific knowledge on the Web is published as passive assertions and cannot decide when to validate evidence, reconcile contradictions, or update confidence as findings accumulate. Curation depends on centralised middleware and institutional continuity, but when registries close, active stewardship stops even when data remain online. We advance the concept of Autonomous FAIR Digital Objects (aFDOs) from an abstract idea to an operational model, to offer a route from passive scientific publication toward accountable, standards-aligned automation that can outlive its publishing institutions. aFDO augments FDOs with three capabilities anchored in Semantic Web standards, namely 1) a policy layer over RDF-star aligned with PROV-O, SHACL, and ODRL for portable condition-action rules, 2) an announcement layer over ActivityStreams 2.0 that bounds per-announcement evaluation cost, and 3) an agreement layer that resolves multi-source contradictions through reputation and confidence weighted agreement under a bounded adversarial model. We provide a formal definition that distinguishes policy specifications, event handlers, and communication interfaces. We evaluate an open reference implementation on 4,305 FDOs grounded in rare-disease ontologies, namely ClinVar, HPO, and Orphanet, combined with controlled synthetic observations. The consensus mechanism resolves 56.3% of 3,914 naturally occurring ClinVar conflicts where multiple submitters disagree and an expert panel has subsequently adjudicated. Under Sybil, collusion, and poisoning attacks, the mechanism degrades gracefully within its design Byzantine-tolerance bound (f < n/5), and fails as predicted beyond that bound.

Problem

• FAIR Digital Objects
• Autonomous Knowledge
• Scientific Data Curation
• Semantic Web
• Conflict Resolution

Innovation

• Autonomous FAIR Digital Objects
• Semantic Web
• Conflict Resolution
• Byzantine Fault Tolerance
• Policy-aware Data Objects