2nd International Workshop on Hybrid Artificial Intelligence and Enterprise Modelling for Intelligent Information Systems (in conjunction with CAiSE'24)

Hybrid Artificial Intelligence is the research direction that focuses on the combination of two prominent fields: sub-symbolic AI (e.g., machine learning like neural networks, Large Language Models, generative AI) and symbolic AI (e.g., knowledge representation and reasoning, knowledge-based systems, knowledge graphs). Approaches from both fields have complementary strengths and enable the creation of Intelligent Information Systems (IIS). For example, whilst neural networks can recognize patterns in large amounts of data, knowledge-based systems contain domain knowledge and enable logical reasoning, enforcement of constraints, and explainability of conclusions. AI approaches are typically integrated with application systems, which provide data for the AI approaches and use the results of these approaches for further processing. Thus, the creation of IIS requires high expertise in both AI approaches, familiarity with the application domain and IT requirements. An early inclusion of domain experts in the engineering process is beneficial as it promotes high quality. Such an early inclusion is, however, challenging because stakeholders from business and IT have complementary skills and speak different languages: one more technical and one more business-oriented. Enterprise Modelling (EM) can tackle this challenge as it supports business and IT alignment. It is an established approach for the conceptual representation, design, implementation, and analysis of information systems. This is of relevance for AI approaches. Graphical notation of enterprise models fosters human interpretability, hence supporting communication and decision-making involving stakeholders from the application domain, IT and AI. The convergence of Hybrid Artificial Intelligence and Enterprise Modelling promises to deliver high value in the creation of Intelligent Information Systems.

This workshop aims to bring together researchers and practitioners from Machine Learning, Knowledge Representation and Reasoning (incl. Semantic Technologies), and Enterprise Modelling to reflect on how combining the three fields can contribute to intelligent information systems engineering.

The setup is such that an ample part of the workshop is dedicated to discussions to identify the need for further applied research. The discussion will be moderated and facilitated by the co-chairs in a panel discussion, where research and industry experts from various fields will confront each other.

Website: https://hybridaims.com/

Submission Guidelines

In this workshop, we welcome full research papers (12 pages) and short (position) papers (6 pages). The accepted papers will be presented in time slots of 20 minutes for regular papers and 15 minutes for short papers. The quality of this workshop will be ensured by having each contribution reviewed by at least three experts in the field.

The papers will be published in proceedings in Springer LNBIP series and, thus, will be indexed.

Committees

Program Committee

• tba

Organizing committee

• Dr. Emanuele Laurenzi (main chair), FHNW University of Applied Sciences and Arts Northwestern Switzerland.
• Prof. Dr. Hans Friedrich Witschel, FHNW University of Applied Sciences and Arts Northwestern Switzerland.
• Dr. Alessandro Oltramari, Bosch, USA. 
• Prof. Dr. Paulo Shakarian, Arizona State University, USA.
• Dr. Peter Haase, Metaphacts, Germany.

Venue

The workshop will be held in Limassol, Cyprus. 

Contact

All questions about submissions should be emailed to info@hybridaims.com