Context modeling with situation rules for industrial maintenance
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) |
| Publisher | IEEE |
| Pages | 1-9 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-1-5090-1314-2 |
| DOIs | |
| Publication status | Published - 7 Nov 2016 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Emerging Technologies and Factory Automation - Duration: 1 Jan 2014 → … |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 1946-0740 |
Conference
| Conference | IEEE International Conference on Emerging Technologies and Factory Automation |
|---|---|
| Period | 1/01/14 → … |
Abstract
Industrial maintenance requires not only experienced service personnel to carry out the tasks but also up-to-date information about the target equipment and its environment. Accessing information required to execute the tasks is a common challenge for maintenance personnel. This paper presents a knowledge modeling approach and a technical architecture of a gateway system developed to support maintenance personnel with information combined from legacy data sources as well as from context ontology augmented with situational knowledge. The novelty of the approach is its unified object oriented style of knowledge representation encapsulating predefined queries and rules into ontology classes. The approach utilizes standard Semantic Web technologies, especially SPARQL query language and SPARQL Inferencing Notation SPIN. Feasibility of the approach is demonstrated with a simple maintenance use case example executed in an experimental knowledge gateway system.
Keywords
- internetworking, knowledge representation, maintenance engineering, object-oriented methods, ontologies (artificial intelligence), personnel, production engineering computing, query languages, query processing, semantic Web, software maintenance, SPARQL Inferencing Notation SPIN, SPARQL query language, context modeling, context ontology, gateway system technical architecture, industrial maintenance, knowledge gateway system, knowledge modeling approach, legacy data source, maintenance personnel, predefined query encapsulation, situation rule, situational knowledge, standard Semantic Web technologies, target equipment, unified object oriented style, Cognition, Computational modeling, Context, Context modeling, Maintenance engineering, Object oriented modeling, Ontologies