Template libraries for industrial asset maintenance: A methodology for scalable and maintainable ontologies

Daniel P. Lupp, Melinda Hodkiewicz, Martin G. Skjæveland

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


Data in engineering industries is highly standardized and information-dense, and usually distributed across many different sources with incompatible formats and implicit semantics. As such, it provides an ideal use case for application of semantic technologies for data integration and access. However, adoption of semantic technologies is hampered due to the complex set of competencies required to construct a scalable and maintainable ontology. We present a methodology for aiding domain experts and ontology engineers in constructing and maintaining industry-viable ontologies using a template-based approach for ontology modeling and instantiation. Using the OTTR framework, the structure of the input formats and the semantics of the target domain are modeled and maintained in separate modularized template libraries. Data provided by domain experts is mapped to these templates, allowing end-users to extend and amend the model without the need to directly interact with semantic technology languages and tools. Our approach is demonstrated on a real-world use case from the asset maintenance domain which has applicability to a wide range of industries.

Original languageEnglish
Pages (from-to)49-64
Number of pages16
JournalCEUR Workshop Proceedings
Publication statusPublished - 2020
Event12th International Workshop on Scalable Semantic Web Knowledge Base Systems, SSWS 2020 - Athens, Greece
Duration: 2 Nov 2020 → …


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