让科学数据自己会判断、会协调、会更新，告别依赖机构的静态发布。
Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge
- 用语义网标准构建可迁移的规则、通知和共识机制
- 在真实罕见病数据上解决56.3%的提交冲突，且抗攻击能力可控
- 适合需要长期可信数据管理的研究者与数据平台
科学知识在网页上通常以被动声明形式发布，无法自主决定何时验证证据、调和矛盾或随新发现更新可信度。当前的数据维护依赖中心化中间件和机构持续性，一旦注册表关闭，即使数据仍在，主动管理也会停止。本文将自主式可查找、可访问、可互操作、可重用数字对象（aFDO）从抽象概念推进为可操作模型，实现从被动发表向可问责、符合标准的自动化演进，使其能超越原始发布机构的生命周期。aFDO在传统FDO基础上增强三项基于语义网标准的能力：1）基于RDF-star的策略层，与PROV-O、SHACL、ODRL对齐，支持可移植的条件-动作规则；2）基于ActivityStreams 2.0的通知层，控制单条公告评估开销；3）协议层，在有限对抗模型下，通过声誉与置信度加权达成多源矛盾的共识。我们给出了形式化定义，区分策略规范、事件处理器与通信接口。在4,305个基于罕见病本体（ClinVar、HPO、Orphanet）的FDO上，结合受控合成观测数据进行评估。共识机制成功解决了3,914个自然发生的ClinVar冲突中的56.3%，这些冲突经专家小组裁定。在Sybil、合谋与污染攻击下，系统表现平稳，符合设计的拜占庭容错边界（f < n/5），超出该边界时按预期失败。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。