HybridAIMS 2025 – 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.

Program tbd

Objective and Topics

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.

Potential topics include (but not limited to):

Hybrid AI-based Information Systems Engineering
Neural-Symbolic Reasoning and Learning
Large Language Models and Knowledge Graphs
Hybrid Artificial Intelligence and Human-in-the-Loop Systems
Hybrid Artificial Intelligence in and for Enterprise Architecture
Hybrid Artificial Intelligence for Business Process Management
Hybrid AI and graphical models for ontology learning
Hybrid recommender systems
Machine Learning, Deep Learning and Neural Networks and Human-in-the-Loop Systems
Machine Learning for Knowledge Graphs and/or ontology-based models
Machine learning in ontology-based Case-Based Reasoning
Commonsense reasoning and Explainable AI
Low code approaches for, e.g., Knowledge Graphs, Machine Learning, knowledge engineering, Hybrid AI engineering
Visual conceptual models for, e.g., ontology constraints, knowledge graph embeddings, machine learning, knowledge engineering
Knowledge Engineering, Representation and Reasoning and Visual Conceptual Models
Ontologies and graphical models for case-based reasoning
Semantic technologies for actionable enterprise models
Combining ontology-based business process and data-driven approaches
Enterprise AI

Panel Discussion

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. More details to be disclosed.

Important Dates

Paper submission deadline: 07 March 2025
Authors notification: 07 April 2025
Camera-ready deadline: 14 April 2025
Author registration deadline: 14 April 2025
Workshop date: 16-17 June 2024

Submissions

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 two experts in the field.

The papers will be published in proceedings in Springer LNBIP series.

To submit your paper, you must use the EasyChair site of the CAiSE 2025 conference through the track “Hybrid Artificial Intelligence and Enterprise Modelling for Intelligent Information Systems (HybridAIMS2025)”. Submissions must conform to Springer, LNCS format.

Submit Paper