论文标题
一种基于可行的知识图的认知方法，用于支持维护操作
A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations
论文作者
论文摘要
在工业4.0时代，认知计算及其促成技术（人工智能，机器学习等）允许定义能够通过在正确的时间提供相关信息，从结构化公司的数据库中检索到的相关信息，并从结构化公司的数据库和非结构化文档中检索到能够支持维护。此外，上下文信息在计划和执行干预过程中量身定制支持方面起着至关重要的作用。可以借助传感器，可穿戴设备，室内和室外定位系统以及对象识别功能（使用固定或可穿戴式摄像头）来检测上下文信息，所有这些都可以收集历史数据以进行进一步分析。在这项工作中，我们提出了一个认知系统，该系统从过去的干预措施中学习，以产生上下文建议，以改善时间，预算和范围的维护实践。该系统使用正式的概念模型，增量学习和排名算法来实现这些目标。
In the era of Industry 4.0, cognitive computing and its enabling technologies (Artificial Intelligence, Machine Learning, etc.) allow to define systems able to support maintenance by providing relevant information, at the right time, retrieved from structured companies' databases, and unstructured documents, like technical manuals, intervention reports, and so on. Moreover, contextual information plays a crucial role in tailoring the support both during the planning and the execution of interventions. Contextual information can be detected with the help of sensors, wearable devices, indoor and outdoor positioning systems, and object recognition capabilities (using fixed or wearable cameras), all of which can collect historical data for further analysis. In this work, we propose a cognitive system that learns from past interventions to generate contextual recommendations for improving maintenance practices in terms of time, budget, and scope. The system uses formal conceptual models, incremental learning, and ranking algorithms to accomplish these objectives.
