Abstract

We present an approach to publish Linked Data on the Web with quantifiable integrity using Web technologies, and in which rational agents are incentivised to contribute to the integrity of the link network. To this end, we introduce self-verifying resource representations, that include Linked Data Signatures whose signature value is used as a suffix in the resource’s URI. Links among such representations, typically managed as web documents, contribute therefore to preserving the integrity of the resulting document graphs. To quantify how well a document’s integrity can be relied on, we introduce the notion of trust scores and present an interpretation based on hubs and authorities. In addition, we present how specific agent behaviour may be induced by the choice of trust score regarding their optimisation, e.g., in general but also using a heuristic strategy called Additional Reach Strategy (ARS). We discuss our approach in a three-fold evaluation: First, we evaluate the effect of different graph metrics as trust scores on induced agent behaviour and resulting evolution of the document graph. We show that trust scores based on hubs and authorities induce agent behaviour that contributes to integrity preservation in the document graph. Next, we evaluate different heuristics for agents to optimise trust scores when general optimisation strategies are not applicable. We show that ARS outperforms other potential optimisation strategies. Last, we evaluate the whole approach by examining the resilience of integrity preservation in a document graph when resources are deleted. To this end, we propose a simulation system based on the Watts–Strogatz model for simulating a social network. We show that our approach produces a document graph that can recover from such attacks or failures in the document graph.

中文翻译：

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网络上链接数据的可量化完整性

 抽象的

我们提出了一种使用网络技术在网络上发布具有可量化完整性的链接数据的方法，其中理性代理被激励为链接网络的完整性做出贡献。为此，我们引入了自验证资源表示，其中包括链接数据签名，其签名值用作资源 URI 中的后缀。因此，这些表示之间的链接（通常作为网络文档进行管理）有助于保持结果文档图的完整性。为了量化文档完整性的可信度，我们引入了信任评分的概念，并根据中心和权威机构提出了解释。此外，我们还介绍了如何通过选择有关其优化的信任评分来诱导特定的代理行为，例如，通常但也使用称为附加覆盖策略（ARS）的启发式策略。我们通过三重评估讨论我们的方法：首先，我们评估不同图指标作为信任分数对诱导代理行为和文档图的演化的影响。我们表明，基于中心和权威的信任评分会诱导代理行为，从而有助于文档图中的完整性保存。接下来，我们评估代理的不同启发式方法，以在一般优化策略不适用时优化信任分数。我们证明 ARS 优于其他潜在的优化策略。最后，我们通过检查删除资源时文档图中完整性保存的弹性来评估整个方法。为此，我们提出了一种基于 Watts-Strogatz 模型的模拟系统，用于模拟社交网络。 我们表明，我们的方法生成的文档图可以从文档图中的此类攻击或故障中恢复。