# Extracting Semantics from Maintenance Records

arXiv: https://arxiv.org/abs/2108.05454 · HTML: https://mlsanity.com/paper/2108.05454/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Justification-Based Reliability in Machine Learning](https://mlsanity.com/paper/1911.07391/) (2019)

## Built on by (most cited first, 1 of 1)

- [Trusted Knowledge Extraction for Operations and Maintenance Intelligence](https://mlsanity.com/paper/2507.22935/) (2025)

## Abstract (arXiv)

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.