  Rapid progress in natural language processing has led to its utilization in a
variety of industrial and enterprise settings, including in its use for
information extraction, specifically named entity recognition and relation
extraction, from documents such as engineering manuals and field maintenance
reports. While named entity recognition is a well-studied problem, existing
state-of-the-art approaches require large labelled datasets which are hard to
acquire for sensitive data such as maintenance records. Further, industrial
domain experts tend to distrust results from black box machine learning models,
especially when the extracted information is used in downstream predictive
maintenance analytics. We overcome these challenges by developing three
approaches built on the foundation of domain expert knowledge captured in
dictionaries and ontologies. We develop a syntactic and semantic rules-based
approach and an approach leveraging a pre-trained language model, fine-tuned
for a question-answering task on top of our base dictionary lookup to extract
entities of interest from maintenance records. We also develop a preliminary
ontology to represent and capture the semantics of maintenance records. Our
evaluations on a real-world aviation maintenance records dataset show promising
results and help identify challenges specific to named entity recognition in
the context of noisy industrial data.
