The 44th International Conference on Conceptual Modeling (ER 2025) is the premier international conference for research and practice on Conceptual Modelling. The conference provides a vibrant forum for discussing and extending the state-of-the-art conceptual modeling foundations, emerging and future challenges, and the pivotal role conceptual modeling plays in a variety of applications. In celebrating its 44th anniversary this year, we especially invite contributions on the theme of:

BUILDING TRUST THROUGH CONCEPTUAL MODELING

Building trust in digital ecosystems has gained a heightened importance in an increasingly contested world. This year’s theme focuses on the important role conceptual modeling plays in creating systems that are trustworthy, inclusive, and transparent. We invite the Conceptual Modelling community to deliberate on how traditional modeling principles and frameworks can contribute and adapt to new advancements in AI, data ecosystems, and autonomous platforms while upholding ethical standards and fostering trust in digital innovations.

We welcome submissions of original research on a variety of topics on conceptual modeling, including well-established and emerging areas of research and practice, as well as submissions that lead to new foundations, links, applications, or enlarge current boundaries of conceptual modeling. We also invite industry reports and vision papers.

Specific examples of relevant topics include but are not limited to:

Foundations of conceptual modeling:

    Human-centred and inclusive modeling
    Model explainability and transparency
    Role of modeling in engendering trust and building trustworthy systems
    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
    Interactive, dynamic and adaptive modeling systems
    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 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
    Data-centric AI development
    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
    Synthetic data and simulation modeling
    Other data spaces

Conceptual modeling in:

    AI, data mining, data science, machine learning, explainable AI, LLMs, statistics
    Business, climate, compliance, economics, education, energy, entertainment, government, health, law, sustainability, etc
    Collaboration, crowdsourcing, games, and social networks
    Business intelligence and analytics, Data warehousing
    Engineering, such as agile development, requirements engineering, reverse engineering, model-driven engineering
    Enterprises, including the modeling of business rules, capabilities, goals, services, processes, values, software, and systems
    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
    Metaverse and Extended Reality (XR)

Conceptual modeling showcased by:

    Computational tools that advance the state-of-the-art
    Ethnographic, qualitative, empirical case studies, and experience reports of applications
    Comparative and benchmarking studies