Conference item
SemDia: Semantic rule-based equipment diagnostics tool
- Abstract:
- Rule-based diagnostics of power generating equipment is an important task in industry. In this demo we present how semantictechnologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We present our diagnostic system SemDia. The attendees will be able to write diagnostic programs in SemDia using sigRL over 50 Siemens turbines. We also present how such programs can be automatically verified for redundancy and inconsistency. Moreover, the attendees will see the provenance service that SemDia provides to trace the origin of diagnostic results.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
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(Preview, Accepted manuscript, pdf, 766.0KB, Terms of use)
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- Publisher copy:
- 10.1145/3132847.3133191
Authors
- Publisher:
- Association for Computing Machinery
- Host title:
- 26th ACM International Conference on Information and Knowledge Management (CIKM 2017)
- Journal:
- 26th ACM International Conference on Information and Knowledge Management (CIKM 2017) More from this journal
- Publication date:
- 2017-11-01
- Acceptance date:
- 2017-08-05
- DOI:
- Keywords:
- Pubs id:
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pubs:730816
- UUID:
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uuid:fc4f01b9-d57e-483d-874b-45c3ba4fdf15
- Local pid:
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pubs:730816
- Source identifiers:
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730816
- Deposit date:
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2017-09-27
Terms of use
- Copyright holder:
- *Copyright holder name ("et al" as required)*
- Copyright date:
- 2017
- Notes:
- © 2017 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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