Conference Information

ER 2026: International Conference on Conceptual Modeling

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Submission Date:
2026-05-05
Notification Date:
2026-07-14
Conference Date:
2026-10-05
Location:
St. John's, Canada
Years:
45
CCF: c   CORE: a   QUALIS: a2   Viewed: 117588   Tracked: 43   Attend: 10

Call For Papers

ER 2026 (International Conference on Conceptual Modeling) is a CCF C / CORE A / QUALIS A2 conference held in St. John's, Canada on 2026-10-05. The paper submission deadline is 2026-05-05. Acceptance notifications are sent on 2026-07-14.

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
Last updated by Dou Sun in

Acceptance Ratio

Average acceptance rate: 22.3% over 6 years (2000–2005).

YearSubmittedAcceptedAccepted(%)
20051693118.3%
20042935719.5%
20031533824.8%
20021303023.1%
20011823921.4%
20001403726.4%

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