AI 中文总结
研究针对ODRL策略语言无数学形式语义致系统互操作性受限问题，基于现有语义模型将其评估问题形式化，提供新算法与实现，展示首个具透明语义且支持所有规则类型的评估器，实验测性能并与现有评估器比较。
AI 中文摘要
ODRL策略语言正成为欧洲数据空间中策略建模数据访问和使用偏好、人工智能治理策略及数据工作流程的事实上的标准。当前标准没有数学形式语义来描述系统应如何实现策略评估，导致多种自行解释该语言的系统和工具，限制了互操作性且无法保证一致结果。基于现有ODRL语义模型，我们将ODRL评估问题形式化，涵盖访问控制和监控场景的静态与流设置，提供了新颖高效的算法及实现。我们展示了首个具有透明形式语义且支持所有规则类型的ODRL评估器，通过实验测量其性能，分析与策略复杂性及评估数据大小相关的不同可扩展性维度，并与现有ODRL评估器进行比较。
英文摘要
The ODRL policy language is emerging as the de-facto standard for policy modelling data access and usage preferences, AI governance policies and data workflows in European dataspaces. The current standard has no mathematical formal semantics to describe how a system should implement policy evaluation. This has resulted in a variety of systems and tools that implement their own interpretation of the language, which limits interoperability and cannot guarantee consistent results. Based on an existing semantic model of ODRL, we formalise the problems of ODRL evaluation for the access control and monitoring scenarios, in both static and streaming settings, and we provide a novel, efficient algorithm and implementation. We present the first ODRL Evaluator with transparent formal semantics and supporting all rule types. We experimentally measure its performance, analysing different scalability dimensions related to policy complexity and size of the data on which a policy is evaluated. We compare our system with the state-of-the-art by providing a comparative review of existing ODRL evaluators, which highlights the differences in supported ODRL features and evaluation modes.
