这篇论文提出了 COntExt，用运行指标自动帮你扩展本体，不用再手动梳理概念关系，四个安全本体上验证过效果。
COntExt 是一个面向上下文感知的本体扩展框架，输入结构化指标定义，输出应整合进现有本体的概念与属性建议。该框架将扩展任务拆分为父类预测、关系类型预测和数据属性分配三个子任务。研究者在四个网络安全本体上评估了多种算法，结果显示指标上下文在关系类型预测和数据属性分配上优于仅依赖本体上下文的基线。论文表明运行指标目录是实用且未被充分利用的本体扩展来源，可显著降低本体维护成本。
COntExt: Towards Context-Aware Ontology Extension from Operational Metrics
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.