-------- Forwarded Message -------- Subject: [AISWorld] call for papers - HybridAIMS'23 Date: Thu, 9 Mar 2023 06:53:42 +0000 From: Emanuele Laurenzi emanuele.laurenzi@fhnw.ch To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org, aisworld-owner@lists.aisnet.org aisworld-owner@lists.aisnet.org
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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
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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. _______________________________________________________________________________ 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. _______________________________________________________________________________ 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
_______________________________________________________________________________ HybridAIMS 2023 ORGANIZING COMMITTEE Co-Chairs: Emanuele Laurenzi Hans Friedrich Witschel Peter Haase
Contact: info@hybridaims.commailto:info@hybridaims.com _______________________________________________________________________________
----------------------------------------------------------------------------------------------------- FHNW - University of Applied Sciences and Arts Northwestern Switzerland School of Business
Dr. Emanuele Laurenzihttps://www.fhnw.ch/en/people/emanuele-laurenzi, Lecturer and Senior Researcher in Intelligent Information Systems & Innovation PhD in Information Systems
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T +41 76 789 33 16 P +41 62 957 28 26 emanuele.laurenzi@fhnw.chmailto:emanuele.laurenzi@fhnw.ch
https://www.fhnw.ch/en/people/emanuele-laurenzi
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