[AISWorld] Fwd: Fw: Call for Papers: 1st International Workshop on Hybrid Artificial Intelligence and Enterprise Modelling for Intelligent Information Systems (in conjunction with CAiSE 2023)

Mon Jan 23 06:28:30 EST 2023

*HybridAIMS’23*

*1st International Workshop on Hybrid Artificial Intelligence and
Enterprise Modelling for Intelligent Information Systems*

*In conjunction with the 35th International Conference on Advanced
Information Systems Engineering CAiSE 2023
<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcaise23.svit.usj.es%2F&data=05%7C01%7Calta.vandermerwe%40fhnw.ch%7C73a0412c8ecb4402edd008dafb00a514%7C9d1a5fc8321e4101ae63530730711ac2%7C0%7C0%7C638098277428125950%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=jlTiO7yGQIcHNf1l3MPvkUAjUweR2XEaB7oGjlB%2FBCg%3D&reserved=0>*

*12th June 2023*
Zaragoza, Spain.

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.

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 intelligent information systems engineering.

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 top world-class experts from our Programme
Committee
<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fhybridaims.com%2F%23%3A~%3Atext%3DSubmit%2520Paper-%2CProgramme%2520Committee%2C-Karsten%2520B%25C3%25B6hm%252C%2520University&data=05%7C01%7Calta.vandermerwe%40fhnw.ch%7C73a0412c8ecb4402edd008dafb00a514%7C9d1a5fc8321e4101ae63530730711ac2%7C0%7C0%7C638098277428282191%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=FHXFxKMD4HkWO5V9OkwKGoCqM89DJdskTQAOkcWVvMc%3D&reserved=0>.

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 *World-Café
style*, where groups will be formed to brainstorm the resolution of current
real-world challenges across industries.

To submit your paper you must use the EasyChair site of the CAiSE 2023
<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Dcaise2023&data=05%7C01%7Calta.vandermerwe%40fhnw.ch%7C73a0412c8ecb4402edd008dafb00a514%7C9d1a5fc8321e4101ae63530730711ac2%7C0%7C0%7C638098277428282191%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=QZsBHm9XrIcu8vU9RxyoiCB%2FesJ1SdxtyJG9XhUPEgg%3D&reserved=0>
conference through the track “Hybrid Artificial Intelligence and Enterprise
Modelling for Intelligent Information Systems (HybridAIMS23)”. The accepted
papers will be published in proceedings in Springer LNBIP series
<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.springer.com%2Fseries%2F7911&data=05%7C01%7Calta.vandermerwe%40fhnw.ch%7C73a0412c8ecb4402edd008dafb00a514%7C9d1a5fc8321e4101ae63530730711ac2%7C0%7C0%7C638098277428282191%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=eXJmDiAUc1ht8jLSl1D2IFU7uOXEMZWASAlaEbwz3CM%3D&reserved=0>
and, thus, will be indexed. Submissions should not be submitted or
published elsewhere, and must conform to Springer, LNCS format
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.

Important Dates:

   - Paper submission deadline: 7 March 2023
   - Authors notification: 4 April 2023
   - Camera-ready: 4 May 2023
   - Workshop date: 12 June 2023

We look forward to receiving your papers documenting original research
aligned with the HybridAIMS’23 workshop.

Potential topics include (but 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

 For more information, please visit the HybridAIMS’23 website at
https://hybridaims.com/
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Please refer to 
http://upnet.up.ac.za/services/it/documentation/docs/004167.pdf 
<http://upnet.up.ac.za/services/it/documentation/docs/004167.pdf> for
full 
details.
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