Scientific Workshop HybridAIMS

HybridAIMS 2026 – in conjunction with the 38th International Conference on Advanced Information Systems Engineering (CAiSE 2026)

Hybrid Artificial Intelligence is the research direction that focuses on the combination of two prominent fields, i.e., 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 engineering, knowledge-based systems). 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 that are useful for organizations.

In this workshop, we welcome full research papers and short papers that deal with the three fields Machine Learning, Knowledge Representation and Reasoning, and Enterprise Modelling. The setup is such that a large part of the workshop is dedicated to discussions to identify the need for further applied research.

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 engineering intelligent information systems that are useful for organizations.

Potential topics include (but not limited to):

Hybrid AI-reliant Information Systems
Hybrid LLM-Ontology/Knowledge Graph Architectures
Hybrid AI for Agents / Agentic AI / Agentic Workflows / Multi-Agent Systems / LLM-based Agent Architectures
Neuro-Symbolic Reasoning and Learning in Information Systems
Large Language Models and Knowledge Graphs for Information Systems
Hybrid AI and Human-in-the-Loop Systems
Hybrid AI in/for Enterprise Architectures
Hybrid AI in/for Business Processes
Hybrid AI in/for Knowledge Engineering
Hybrid AI in/for Agile Business or Business Innovation or new Business Models
Hybrid AI-powered recommender systems
Machine Learning, Neural Networks, Deep Learning, Reinforcement Learning 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, and knowledge engineering
Knowledge Engineering, Representation and Reasoning combined with visual conceptual models
Ontologies and graphical models for case-based reasoning
Semantic technologies for actionable enterprise models
Integrating ontology-based business process and data-driven approaches

Important Dates

Paper submission deadline: 06 March 2026
Notification of acceptance: 04 April 2026
Camera-ready deadline: 22 April 2026
Workshop date: 08-09 June 2026

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 three 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 2026 conference through the track “Hybrid Artificial Intelligence and Enterprise Modelling for Intelligent Information Systems (HybridAIMS2026)”. Submissions must conform to Springer, LNCS format.

Valuable research papers which did not make it to the Springer publication will be invited to be published as Short Papers (9 pages) in the CEUR volume affiliated with CAiSE. In this case, submissions will have to conform to CEUR format.

Submit Paper