Maintenance has largely remained a human-knowledge centered activity, with the primary records of activity being textbased maintenance work orders (MWOs). However, the bulk of maintenance research does not currently attempt to quantify human knowledge, though this knowledge can be rich with useful contextual and system-level information. The underlying quality of data in MWOs often suffers from misspellings, domain-specific (or even workforce specific) jargon, and abbreviations, that prevent its immediate use in computer analyses. Therefore, approaches to making this data computable must translate unstructured text into a formal schema or system; i.e., perform a mapping from informal technical language to some computable format. Keyword spotting (or, extraction) has proven a valuable tool in reducing manual efforts while structuring data, by providing a systematic methodology to create computable knowledge. This technique searches for known vocabulary in a corpus and maps them to designed higher level concepts, shifting the primary effort away from structuring the MWOs themselves, toward creating a dictionary of domain specific terms and the knowledge that they represent. The presented work compares rules-based keyword extraction to data-driven tagging assistance, through quantitative and qualitative discussion of the key advantages and disadvantages. This will enable maintenance practitioners to select an appropriate approach to information encoding that provides needed functionality at minimal cost and effort.
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Production, Maintenance, NLP, Manufacturing
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