A major portion of industrial maintenance data is in unstructured form, which makes its organization, search, and reuse very challenging. For this reason, the knowledge embedded in historical maintenance data is seldom analyzed or reused for purposes such as root cause analysis, failure prevention, and maintenance diagnostics. If the valuable knowledge patterns nested in maintenance data are identified, liberated, and formalized, they can significantly improve the intelligence of maintenance management systems by providing actionable insights. The objective of this research is to help advance the progression from data to information and knowledge through data-driven creation of a public and open-source knowledge graphs built from the textual data available in maintenance workorders. A SKOS-based thesaurus is used to support automated entity extraction from the text. A formal OWL-based ontology provides the semantic schema of the knowledge graph. A software tool (KnoWo) is developed to streamline the text-to-graph translation process. It was observed that the proposed text-to-graph tool chain improves knowledge discovery by analyzing maintenance logs.