A hybrid approach for developing, extending, and implementing industrial maintenance knowledge graphs and semantic ontologies to support smart maintenance diagnostics
Date
2022-12
Authors
Tahsin, Renita
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Abstract
The unstructured historical data stored in Computerized Maintenance
Management Systems (CMMS) is a mine of maintenance diagnostic information. This
data is often underused due to its unstructured and informal nature. This thesis will
propose a framework for transforming maintenance log data, which is often in the form
of natural language text, into formal knowledge graphs. The proposed method generates a
knowledge graph that encodes the semantic relationships between multiple maintenance
entities based on the historical data that can be found in maintenance work orders. The
knowledge graph is created semi-automatically through the hybrid application of text
analytics techniques and human-assisted semantic tagging of maintenance work order
text. The semantics of the knowledge graph proposed in this research will be provided
jointly by a SKOS thesaurus and an OWL ontology. SKOS (Simple Knowledge
Organization System) and OWL (Web Ontology Language) are both Semantic Web
standards that will enhance the reusability and portability of the final knowledge graph.
The knowledge graph created as an output of a java based tool will become an opensource shared industrial maintenance knowledge base that can be extended incrementally
and be used for various decision support applications, including maintenance diagnostics
and root-cause analysis. An online knowledge graph platform will be used to conduct
querying and inferencing over the graph to support smart maintenance diagnosis.
Description
Keywords
Knowledge graph, Thesaurus, Natural Language Processing, Ontology
Citation
Tahsin, R. (2022). <i>A hybrid approach for developing, extending, and implementing industrial maintenance knowledge graphs and semantic ontologies to support smart maintenance diagnostics</i> (Unpublished thesis). Texas State University, San Marcos, Texas.