DCR-KiPN a hybrid modeling approach for knowledge-intensive processes
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Hybrid modeling approaches have been proposed to represent processes that have both strictly regulated parts and loosely regulated parts. Such process is so-called Knowledge-intensive Process (KiP), which is a sequence of activities based on intense knowledge use and acquisition. Due to these very particular characteristics, the first author previously proposed the Knowledge-intensive Process Ontology (KiPO) and its subjacent notation (KiPN). However, KiPN still fails to represent the declarative perspective of a KiP. Therefore, in this paper, we propose to improve KiPN by integrating it with the declarative process modeling language DCR Graphs. DCR-KiPN is a hybrid process modeling notation that combines a declarative process model language (activities and business rules) with the main aspects of a KiP, such as cognitive elements (decision rationale towards goals, beliefs, desires and intentions), interactions and knowledge-exchange among its participants.
Original language | English |
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Title of host publication | Conceptual Modeling - 38th International Conference, ER 2019, Proceedings |
Editors | Alberto H.F. Laender, Barbara Pernici, Ee-Peng Lim, José Palazzo M. de Oliveira |
Number of pages | 9 |
Publisher | Springer VS |
Publication date | 2019 |
Pages | 153-161 |
ISBN (Print) | 9783030332228 |
DOIs | |
Publication status | Published - 2019 |
Event | 38th International Conference on Conceptual Modeling, ER 2019 - Salvador, Brazil Duration: 4 Nov 2019 → 7 Nov 2019 |
Conference
Conference | 38th International Conference on Conceptual Modeling, ER 2019 |
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Land | Brazil |
By | Salvador |
Periode | 04/11/2019 → 07/11/2019 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11788 LNCS |
ISSN | 0302-9743 |
- Hybrid process notation, Knowledge-intensive Process
Research areas
ID: 239961804