使用知识图谱和符号查询流程跟踪3

William Van Woensel
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引用次数: 0

摘要

在流程挖掘中,日志探索步骤可以使事件轨迹变得有意义,例如,识别事件模式和不合逻辑的轨迹,并深入了解其可变性。为了支持富有表现力的日志探索,可以将事件日志转换为知识图谱(KG),然后使用通用语言对其进行查询。我们探索了使用资源描述框架(RDF)作为数据模型,结合通用 Notation3(N3)规则语言进行查询的语义 KG 创建方法。我们展示了如何在 N3 中实现受最新技术启发的典型跟踪查询约束。我们将以案例和对象为中心的事件日志转换为基于轨迹的语义 KG;从而将 OCEL2 日志 "扁平化 "为基于对象通过 KG 的路径的轨迹。这种解决方案具有:(a) 表达性,因为查询可以多种方式实例化约束,并可任意约束属性和关系(如行为体、资源);(b) 灵活性,因为 OCEL2 事件日志可以基于 KG 以任意方式序列化为轨迹;(c) 可扩展性,因为其他人可以利用相同的实现模式扩展我们的库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Process Trace Querying using Knowledge Graphs and Notation3
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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