DCR-KiPN a hybrid modeling approach for knowledge-intensive processes

Flávia Santoro*, Tijs Slaats, Thomas T. Hildebrandt, Fernanda Baiao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

5 Citations (Scopus)

Abstract

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 languageEnglish
Title of host publicationConceptual Modeling - 38th International Conference, ER 2019, Proceedings
EditorsAlberto H.F. Laender, Barbara Pernici, Ee-Peng Lim, José Palazzo M. de Oliveira
Number of pages9
PublisherSpringer VS
Publication date2019
Pages153-161
ISBN (Print)9783030332228
DOIs
Publication statusPublished - 2019
Event38th International Conference on Conceptual Modeling, ER 2019 - Salvador, Brazil
Duration: 4 Nov 20197 Nov 2019

Conference

Conference38th International Conference on Conceptual Modeling, ER 2019
Country/TerritoryBrazil
CitySalvador
Period04/11/201907/11/2019
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11788 LNCS
ISSN0302-9743

Keywords

  • Hybrid process notation
  • Knowledge-intensive Process

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