用知识图谱和N3语言实现流程日志的语义化查询，支持灵活分析事件轨迹。
Process Trace Querying using Knowledge Graphs and Notation3
- 将事件日志转为基于RDF的知识图谱，用N3语言表达查询约束
- 支持多方式灵活展开日志轨迹，可任意约束角色、资源等属性
- 适合需要深度分析流程变异性与异常模式的研究者使用
在流程挖掘中，日志探索步骤有助于理解事件轨迹，如识别事件模式、发现逻辑异常并洞察其变化。本文通过资源描述框架（RDF）构建语义知识图谱，并结合通用的Notation3（N3）规则语言进行查询。我们展示了如何将现有前沿的轨迹查询约束以N3形式实现。针对案例中心与对象中心的事件日志（如OCEL2），将其转化为基于对象路径的轨迹式语义知识图谱，实现日志“扁平化”。该方案具备三重优势：(a) 表达性强，可多种方式实例化约束并任意限制属性与关系（如参与者、资源）；(b) 灵活性高，可根据知识图谱任意序列化日志轨迹；(c) 可扩展性好，他人可通过复用相同实现模式扩展本库。
原文摘要 · Abstract (English)
In process mining, a log exploration step allows making sense of the event traces; e.g., identifying event patterns and illogical traces, and gaining insight into their variability. To support expressive log exploration, the event log can be converted into a Knowledge Graph (KG), which can then be queried using general-purpose languages. We explore the creation of semantic KG using the Resource Description Framework (RDF) as a data model, combined with the general-purpose Notation3 (N3) rule language for querying. We show how typical trace querying constraints, inspired by the state of the art, can be implemented in N3. We convert case- and object-centric event logs into a trace-based semantic KG; OCEL2 logs are hereby "flattened" into traces based on object paths through the KG. This solution offers (a) expressivity, as queries can instantiate constraints in multiple ways and arbitrarily constrain attributes and relations (e.g., actors, resources); (b) flexibility, as OCEL2 event logs can be serialized as traces in arbitrary ways based on the KG; and (c) extensibility, as others can extend our library by leveraging the same implementation patterns.
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