COntExt：面向基于运行指标的上下文感知本体扩展
COntExt: Towards Context-Aware Ontology Extension from Operational Metrics
- Know Center Research GmbH
- Software Competence Center Hagenberg GmbH（哈根堡软件能力中心有限公司）
- Fraunhofer AISEC（弗劳恩霍夫应用研究促进协会人工智能与安全研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对运行指标目录与本体知识手动关联的问题，提出COntExt框架，通过父类预测等三个子任务，利用指标上下文扩展本体，实验显示其能以更低成本提升本体扩展效果。
AI中文摘要：
组织越来越多地以结构化、机器可读格式定义运行指标，用于监控系统、流程和合规性。这些指标定义隐含编码了领域知识，如引用的概念、属性和关系，通常超出形式本体所捕获的内容。然而，运行指标目录与本体知识之间的关联目前是手动、临时且劳动密集型的。我们提出COntExt，一个上下文感知本体扩展框架，以结构化指标定义为输入，利用这些指标的上下文，建议如何将引用的概念和属性集成到现有本体中。该框架将扩展问题定义为三个子任务：父类预测、关系类型预测和数据属性分配。我们在四个网络安全本体上评估了每个任务的不同算法。结果表明，在关系类型预测和数据属性分配中，源自指标的上下文比本体上下文基线能提供更好的建议。我们的工作证明，运行指标目录是一种实用且未被充分利用的本体扩展来源，使组织维护本体的成本显著低于手动工程。
英文摘要：
Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metric definitions implicitly encode domain knowledge, such as referencing concepts, properties, and relationships, that often extends what is captured in formal ontologies. Yet the connection between operational metric catalogues and ontological knowledge remains manual, ad-hoc, and labor-intensive. We present COntExt, a framework for context-aware ontology extension that takes structured metric definitions as input and suggests how referenced concepts and properties should be integrated into an existing ontology, utilizing the context of these metrics. The framework defines the extension problem as three sub-tasks: parent class prediction, relation type prediction, and data property assignment. Across four cybersecurity ontologies, we evaluate different algorithms for each task. Our results show that metric-derived context improves the suggestions over ontology-context baselines for relation type prediction and data property assignment. Our work demonstrates that operational metric catalogues are a practical and underexploited source for ontology extension. This work enables organizations to maintain their ontologies at a significantly lower cost than manual engineering.
