Conceptual Modeling, AI, and Beyond

As AI and ML reshape technology, they're also revolutionizing conceptual modeling. We call for papers exploring this change, particularly how AI and ML integrate with and expand conceptual modeling. Submissions should cover established topics like modeling languages, theories, and methods, as well as AI/ML's novel contributions to the field. We encourage papers that push the boundaries of conceptual modeling and demonstrate AI/ML's transformative effects. Contributions should reflect the current state and forecast the future of conceptual modeling, setting the stage for pioneering research. We also invite industry reports, vision papers, and additional “hackathon” papers.

Topics of Interest (Not Limited to the Following)

Foundations of conceptual modeling

    Automated and AI-assisted conceptual modeling
    Complexity management of large conceptual models
    Concept formalization, including data manipulation languages and techniques, formal concept analysis, and integrity constraints
    Domain-specific modeling
    Discovery of models, (anti-)patterns, and structures
    Evolution, exchange, integration and transformation of models
    Justification and evaluation of models
    Logic-based knowledge representation and reasoning
    Multi-level and multi-perspective modeling
    Ontological and cognitive foundations
    Quality paradigms and metrics
    Semantics in conceptual modeling
    Theories and methodologies for conceptual modeling
    Verification and validation of conceptual models

Conceptual modeling in

    Business, climate, compliance, economics, education, energy, entertainment, government, health, law, sustainability, etc.
    Collaboration, crowdsourcing, games, and social networks
    Engineering, such as agile development, requirements engineering, reverse engineering
    Enterprises, including the modeling of business rules, capabilities, goals, services, processes, and values
    Ethics, fairness, responsibility, or trust
    Digital twins, fog and edge computing, Industry 4.0, internet of things
    Information classification, filtering, retrieval, summarization, and visualization
    Scientific data management, including FAIR practices

Conceptual modeling showcased by

    Computational tools that advance the state-of-the-art
    Empirical studies
    Experience reports of applications, use cases, and real-world impact

Conceptual modeling for AI and ML

    Generative AI & Conceptual Modeling
    AI, data mining, data science, machine learning, or statistics
    Domain knowledge representation and understanding for ML training
    Data engineering and domain knowledge engineering for ML training
    Data quality assurance, compliance, and governance
    Conceptual modeling and Explainable AI
    Development of large-language models, foundation models, and federated learning
    Modeling of relational machine learning incl. graph neural networks, inductive logic programming, and statistical relational learning

Conceptual modeling for

    Data access, acquisition, integration, maintenance, preparation, transformation, and visualization
    Data management, including database design, performance optimization, privacy and security, provenance, transactions, queries
    Data value, variety, velocity, veracity, volume, and other dimensions
    Distributed, decentralized, ledger-based, parallel and P2P databases
    Graph and network databases
    Object-oriented and object-relational databases
    SQL, NewSQL and NoSQL databases
    Spatial and temporal databases
    Event-based and stream architectures
    Multimedia and text databases
    Approximate, probabilistic, and uncertain databases
    Web, Semantic Web, knowledge graphs, and cloud databases
    Other data spaces