Modular ontologies for maintenance texts



Maintenance work orders, equipment rebuild reports, investigations, maintenance procedures, and equipment manuals are a vast resource for equipment manufacturers and asset operators.

Recent developments in annotation and the use of deep learning by the NLP-TLP group at UWA are unlocking information captured in these texts enabling entity typing of instance data and the creation of knowledge graphs (KG).

We now have 10,000s of maintenance work order and procedure documents and while we can query them, once in KG format using Cypher, we seek to augment our queries with reasoning based on engineering knowledge.

This talk describes our annotation (using the Redcoat collaborative annotation tool) and KG pipeline (using Echidna); demos are available at The talk also provides an overview of two reference and five modular application ontologies for maintenance texts aligned to both BFO/IOF and ISO 15926-14 top level ontologies. One of our goals is to use these ontologies to improve confidence in the answers to KG queries, as this is very important in making decisions involving maintenance activities on engineering assets.


Useful web sites run by members of the NLP-TLP group:

  • and
  • (this also contains links to papers published by the authors relating to maintenance ontologies)


Authors are Professor Melinda Hodkiewicz, Michael Stewart, Caitlin Woods, Wei Liu, Tim French, Tyler Bikaun, Melinda Hodkiewicz

The NLP-TLP group at UWA was formed in 2019 and has grown rapidly since then on the back of funding from the Australian Government and a number of large resources companies We are active across the domains of entity recognition, lexical normalisation, knowledge graphs, ontologies, annotation, adaptive user interfaces and knowledge representation and reasoning, and are producing prototype tools which are being tested by industry partners. We also have strong links with the IOF and also with the group in Norway developing ISO 15926-14.