Problem
Research questions and friction points this paper is trying to address.
trust and reputation systems
accountability
initial reputation
RepuLink: A Linked Data Platform for Accountable Trust
🤖 AI Summary
RepuLink通过两层信任与声誉模型及后向背书奖惩传播机制，解决了分布式网络中责任归属和新节点初始信誉问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
two-layer trust and reputation model
Backward Endorsement Penalty/Reward Propagation (BEPP/BERP)
Linked Data layer
OWL ontology
RDF projection
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