Knowledge Management Technology Using Ontologies, Cognitive Models and Production Expert Systems
- DOI
- 10.2991/csit-19.2019.49How to use a DOI?
- Keywords
- knowledge management, ontology, cognitive models, expert system, energy security.
- Abstract
The article proposes a knowledge management technology based on the joint use of ontologies, cognitive models and production expert systems. Мethods and tools of semantic modeling (primarily ontological and cognitive), developed in the team of authors, were applied within the framework of a two-level research technology of energy security research. An important components of knowledge management technology are the methods of cognitive modeling and the transformation of cognitive models into production rules of the expert system. The proposed approach provides automation of the analysis and interpretation of cognitive maps using production expert systems, which, in turn, allows to reduce the human factor influence and improve the quality of preparation and substantiation of recommendations for decision making. The stages of the proposed knowledge management technology, the algorithm for transforming the cognitive model, the modification and development of tools to support the proposed technology based on the agent approach are considered. Examples of ontologies, cognitive maps and the results of cognitive maps converting into production rules of an expert system are given. The technology has been tested in studies of energy security problems, but may have wider application.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Liudmila Massel AU - Aleksei Massel AU - Dmitrii Pesterev PY - 2019/12 DA - 2019/12 TI - Knowledge Management Technology Using Ontologies, Cognitive Models and Production Expert Systems BT - Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019) PB - Atlantis Press SP - 279 EP - 284 SN - 2589-4900 UR - https://doi.org/10.2991/csit-19.2019.49 DO - 10.2991/csit-19.2019.49 ID - Massel2019/12 ER -