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                               Call for Papers
                    
                       1st International Workshop on
      Hybrid Artificial Intelligence and Enterprise Modelling (HybridAIMS'23)

                 June 12-13, 2023, Zaragoza, SPAIN
                            

                           https://hybridaims.com/

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IMPORTANT DATES:
- Paper submission: March 19th, 2023
- Notification of acceptance: April 4th, 2023
- Camera Ready: May 4th, 2023

Hybrid Artificial Intelligence is the research direction that focuses on the 
combination of two prominent fields sub-symbolic AI (e.g., machine learning, 
deep learning, neural networks) and symbolic AI (e.g., knowledge graphs, 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 amount of data, knowledge-based systems contain domain knowledge and 
enable logical reasoning 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, knowledge about the application domain
 and IT knowledge. An early inclusion of domain experts in the engineering process is 
beneficial as it promotes a high quality of an IIS and would reduce its building time. 
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.
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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.
To submit your paper you must use the EasyChair site of the CAiSE 2023 conference through the track
 “Hybrid Artificial Intelligence and Enterprise Modelling for Intelligent Information Systems (HybridAIMS23)”. 
Submissions must conform to Springer, LNCS format.
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LIST OF TOPICS
Possible topics include, but are not limited to:

Neuro-Symbolic Artificial Intelligence and Enterprise or Visual Models
Hybrid Artificial Intelligence and Enterprise or Visual Models for Human-in-the-Loop
Hybrid Artificial Intelligence in and for Enterprise Architecture
Hybrid Artificial Intelligence for Business Process Management
Hybrid AI and Visual Models for Ontology Learning
Hybrid Recommender Systems with Visual Models
Machine Learning, Deep Learning, Neural Networks, and Visual Models for Human-in-the-Loop
Machine Learning for Knowledge Graphs or Ontology-based Models
Machine Learning in Ontology-based Case-Based Reasoning
Explainable AI through Enterprise Models
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
Domain-Specific Models for Domain Ontology or for Enterprise Ontology
Knowledge Engineering, Representation and Reasoning and Visual Conceptual Models
Ontologies and Visual Models for Rule-Based or Case-Based Reasoning approaches
Semantic Technologies for actionable Enterprise Models
Combining Ontology-based Business Processes and Data-Driven Approaches
Enterprise AI

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HybridAIMS 2023 ORGANIZING COMMITTEE
Co-Chairs:
Emanuele Laurenzi
Hans Friedrich Witschel
Peter Haase

Contact: info@hybridaims.com
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