3rd International Workshop on Hybrid Artificial Intelligence and Enterprise Modelling (in conjunction with the 37th International Conference on Advanced Information Systems Engineering CAiSE 2025)

Hybrid Artificial Intelligence represents an increasingly emerging research domain that merges two significant branches of artificial intelligence: sub-symbolic AI (e.g., machine learning techniques such as neural networks, large language models, and generative AI) and symbolic AI (e.g., knowledge representation, reasoning, knowledge-based systems, and knowledge graphs). These two paradigms possess complementary strengths, enabling the development of Hybrid AI-based Information Systems. For instance, while neural networks excel at identifying patterns in extensive datasets, symbolic approaches leverage domain-specific knowledge to facilitate logical reasoning, enforce constraints, and enhance the interpretability of conclusions. Typically, AI solutions are integrated within applications that supply the necessary data for analysis and utilize the outcomes of these analyses for subsequent processes. Consequently, constructing such hybrid AI-based information systems demands high expertise in both sub-symbolic and symbolic AI approaches, alongside a deep understanding of the application domain and associated IT requirements. Engaging domain experts early in the development process is advantageous, as it enhances the quality of the resulting system. However, this early collaboration can be challenging due to the differing perspectives and terminologies used by the various stakeholders from business, specific application domains, IT, and AI. Enterprise Modelling (EM), as a scientific discipline, offers a solution to this challenge by facilitating alignment between the business and IT worlds. It provides solid theories for the conceptual representation, design, implementation, and analysis of information systems. The use of graphical notations in enterprise models enhances their interpretability, improving communication and decision-making among the diverse stakeholders. In this context, integrating Hybrid Artificial Intelligence with Enterprise Modelling has the potential to significantly advance the design and implementation of Hybrid AI-based Information Systems, combining the strengths of both fields to deliver systems of high value and utility.

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 engineer hybrid AI-based information systems.

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. Peter Haase, metaphacts, Germany,
• Prof. Dr. Marco Montali, Free University of Bozen-Bolzano, Italy.

Venue

The workshop will be held in Vienna, Austria. 

Contact

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