Semantic Annotation of Heterogeneous Data Sources: Towards an Integrated Information Framework for Service Technicians

Service technicians in the domain of industrial maintenance require extensive technical knowledge and experience to complete their tasks. Some of the needed knowledge is made available as document-based technical manuals or service reports from previous deployments. Unfortunately, due to the great amount of data, service technicians spend a considerable amount of working time searching for the correct information. Another challenge is posed by the fact that valuable insights from operation reports are not yet considered due to insufficient textual quality and content-wise ambiguity. In this work we propose a framework to annotate and integrate these heterogeneous data sources to make them available as information units with Linked Data technologies. We use machine learning to modularize and classify information from technical manuals together with ontology-based autocompletion to enrich service reports with clearly defined concepts. By combining these two approaches we can provide an unified and structured interface for both manual and automated querying. We verify our approach by measuring precision and recall of information for typical retrieval tasks for service technicians, and show that our framework can provide substantial improvements for service and maintenance processes.