We welcome submissions of original research on a variety of topics on conceptual modeling. These include well-established areas of research and practice, such as modeling languages and techniques, model theories, methods and tools for developing, transforming, implementing and communicating conceptual models. Submissions that lead to new foundations, links, applications, or enlarge current boundaries of conceptual modeling are especially welcome.

In celebrating the conference’s 45th anniversary this year, we especially invite contributions on the theme of CONCEPTUAL MODELING AND SUSTAINABILITY . Conceptual modeling plays a critical role in sustainability by offering structured ways to represent and connect complex environmental, social, and economic systems. In a year marked by accelerating climate impacts, policy transitions, and technological change, models help researchers and practitioners align assumptions, compare scenarios, and translate knowledge across disciplines. Continuing a long tradition, ER 2026 will host workshops to be held in conjunction with the main conference. These workshops serve as an intensive collaborative forum for exchanging innovative ideas about conceptual modeling, and for discovering new frontiers for its use. We specifically invite proposals for interactive workshops on new, emerging, or established topics in conceptual modeling, or its application in specific domains. We encourage workshop organizers to include activities that provide their participants with a true workshop experience. Topics of interest include foundations of conceptual models, theories of concepts, ontology-driven conceptual modeling and analysis, methods and tools for developing, communicating, consolidating, and evolving conceptual models, and techniques for transforming conceptual models into effective implementations.

Topics of Interest

Specific examples of many other relevant topics include (but are not limited to):

Foundations of conceptual modeling:

Human-centred and inclusive modeling
Model explainability and transparency
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
Knowledge graphs and reasoning
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 care, law, sustainability, supply chains, etc.
Collaboration, crowdsourcing, games, and social networks
Business intelligence and analytics, Data warehousing
Engineering, such as agile development, requirements engineering, reverse engineering, and 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 scientific data practices

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