Devid Montecchiari

According to our database, Devid Montecchiari authored at least 9 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Supporting Reuse of Business Process Models by Semantic Annotation.

Fabian Baumann, Knut Hinkelmann, Devid Montecchiari

Proceedings of the Advanced Information Systems Engineering Workshops, 2023

Enriching Enterprise Architecture Models with Healthcare Domain Knowledge.

Valeriia Afonina, Knut Hinkelmann, Devid Montecchiari

Proceedings of the Advanced Information Systems Engineering Workshops, 2023

Validating Enterprise Architecture Principles Using Derivation Rules and Domain Knowledge.

Devid Montecchiari, Knut Hinkelmann

Proceedings of the Perspectives in Business Informatics Research, 2023

2022

Towards Ontology-Based Validation of EA Principles.

Devid Montecchiari, Knut Hinkelmann

Proceedings of the Practice of Enterprise Modeling, 2022

2021

Ontology-based Validation of Enterprise Architecture Principles in Enterprise Models.

Devid Montecchiari

Proceedings of the Joint Proceedings of the BIR 2021 Workshops and Doctoral Consortium co-located with 20th International Conference on Perspectives in Business Informatics Research (BIR 2021), 2021

Hybrid Conversational AI for Intelligent Tutoring Systems.

Charuta Pande, Hans Friedrich Witschel, Andreas Martin, Devid Montecchiari

Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), 2021

2020

ArchiMEO: A Standardized Enterprise Ontology based on the ArchiMate Conceptual Model.

Knut Hinkelmann, Emanuele Laurenzi, Andreas Martin, Devid Montecchiari, Maja Spahic, Barbara Thönssen

Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development, 2020

Ontology-Based Visualization for Business Model Design.

Marco Peter, Devid Montecchiari, Knut Hinkelmann, Stella Gatziu Grivas

Proceedings of the Practice of Enterprise Modeling, 2020

2019

Towards an Assistive and Pattern Learning-driven Process Modeling Approach.

Emanuele Laurenzi, Knut Hinkelmann, Stephan Jüngling, Devid Montecchiari, Charuta Pande, Andreas Martin

Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019) Stanford University, 2019