AI 中文总结
RepuLink 提出双层信任声誉模型，通过 BEPP/BERP 实现背书者问责，开发了全栈参考实现及带 OWL 本体的链接数据层，解决传统信任模型的局限。
AI 中文摘要
信任与声誉系统支撑着大型分布式网络中的可靠交互，但传统模型通常仅正向传播信任，无法对背书者为其担保的对象进行问责，也未为新加入节点提供有意义的初始声誉。RepuLink 针对这些局限，提出了双层信任与声誉模型，将直接交互反馈与领域特定背书相结合，关键在于通过反向背书奖惩传播（BEPP/BERP）实现对背书者的问责。本文展示了 RepuLink-Tool，这是该模型的可部署全栈参考实现，应用支持节点间交互、评分与背书，同时通过实时仪表盘和交互式信任网络图跟踪声誉。此外，本文在应用之上引入了新的链接数据层，该层包含轻量级 OWL 本体，涵盖节点、交互、评分、背书、成对信任评估及带有 PROV-O 溯源标注的计算声誉分数，还提供各用户信任网络的动态 RDF 投影（多种序列化格式），以及节点可针对自身数据实时查询的范围限定 SPARQL 端点。
英文摘要
Trust and reputation systems underpin reliable interactions in large, distributed networks. However, conventional models typically propagate trust only forward, offering no accountability for endorsers regarding whom they vouch for, and leaving newly joined nodes without a meaningful initial reputation. RepuLink addresses these limitations by proposing a two-layer trust and reputation model that integrates direct interaction feedback with domain-specific endorsements. Crucially, it holds endorsers accountable via Backward Endorsement Penalty/Reward Propagation (BEPP/BERP). This paper demonstrates RepuLink-Tool, a deployable, full-stack reference implementation of this model. The application enables nodes to interact, rate, and endorse each other, while tracking reputation via a live dashboard and an interactive trust network graph. Furthermore, we introduce a new Linked Data layer built on top of the application. This layer features a lightweight OWL ontology encompassing nodes, interactions, ratings, endorsements, pairwise trust assessments, and computed reputation scores annotated with PROV-O provenance. It also provides an on-the-fly RDF projection of each user's trust network in multiple serialisations, alongside a scoped SPARQL endpoint that nodes can query live against their own data.
CommentsISWC 2026: 25th International Semantic Web Conference, Poster and Demos
