Sexton Rachael, Fuge Mark
Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20871.
Dept. of Mechanical Engineering, University of Maryland, College Park, Maryland 20742.
J Mech Des N Y. 2020;142(3). doi: 10.1115/1.4045686.
Recovering a system's underlying structure from its historical records (also called structure mining) is essential to making valid inferences about that system's behavior. For example, making reliable predictions about system failures based on maintenance work-order data requires determining how concepts described within the work order are related. Obtaining such structural information is challenging, requiring system understanding, synthesis, and representation design. This is often either too difficult or too time-consuming to produce. Consequently, a common approach to quickly eliciting tacit structural knowledge from experts is to gather uncontrolled keywords as record labels-i.e., "tags." One can then map those tags to concepts within the structure and quantitatively infer relationships between them. Existing models of tag similarity tend to either depend on correlation strength (e.g. overall co-occurrence frequencies), or on conditional strength (e.g. tag sequence probabilities). A key difficulty in applying either model is understanding under what conditions one is better than the other for overall structure recovery. In this paper, we investigate the core assumptions and implications of these two classes of similarity measures on structure recovery tasks. Then, using lessons from this characterization, we borrow from recent psychology literature on semantic fluency tasks to construct a tag similarity measure that emulates how humans recall tags from memory. We show through empirical testing that this method combines strengths of both common modeling paradigms. We also demonstrate its potential as a pre-processor for structure mining tasks via a case study in semi-supervised learning on real excavator maintenance work-orders.
从系统的历史记录中恢复其底层结构（也称为结构挖掘）对于对该系统的行为做出有效推断至关重要。例如，基于维护工作订单数据对系统故障进行可靠预测需要确定工作订单中描述的概念之间的关系。获取此类结构信息具有挑战性，需要系统理解、综合和表示设计。这通常要么太难，要么太耗时以至于无法完成。因此，一种从专家那里快速引出隐性结构知识的常见方法是收集不受控制的关键词作为记录标签，即“标签”。然后可以将这些标签映射到结构中的概念，并定量推断它们之间的关系。现有的标签相似性模型往往要么依赖于相关强度（例如总体共现频率），要么依赖于条件强度（例如标签序列概率）。应用这两种模型的一个关键困难在于理解在什么条件下一种模型在整体结构恢复方面比另一种更好。在本文中，我们研究了这两类相似性度量对结构恢复任务的核心假设和影响。然后，借鉴这一特征描述中的经验教训，我们借鉴近期关于语义流畅性任务的心理论文来构建一种标签相似性度量，该度量模拟人类从记忆中回忆标签的方式。我们通过实证测试表明，这种方法结合了两种常见建模范式的优点。我们还通过在真实挖掘机维护工作订单的半监督学习中的案例研究，展示了其作为结构挖掘任务预处理工具的潜力。
