EKAW 2026 (International Conference on Knowledge Engineering and Knowledge Management) is a ICORE B conference held in Turin, Italy on 2026-09-29. The paper submission deadline is 2026-05-15 (extended). Acceptance notifications are sent on 2026-07-10.
The 25th International Conference on Knowledge Engineering and Knowledge Management (EKAW-26) encompasses the diverse realms of eliciting, acquiring, modeling, and managing knowledge in a variety of information objects ranging from taxonomies, to ontologies and knowledge graphs. The conference addresses the pivotal role of knowledge in constructing systems and services for the semantic web, knowledge management, knowledge discovery, information integration, natural language processing, intelligent systems, AI systems in e-business, e-health, humanities, cultural heritage, sustainability and beyond.
This year’s special theme is investigating “New Frontiers in Knowledge Engineering”. Indeed, the current AI technological landscape is marked by major new trends and technologies emerging at an unprecedented pace. Generative AI systems, neuro-symbolic AI, agentic AI, AI regulations are just a few of the ground-breaking, ongoing trends. In such a setting, it is natural for each community to embark in a “soul-searching” and strategic positioning activity: What is our role in AI? What are major current and long-term developments in the field? What are new challenges and opportunities brought about by this context? In this year’s EKAW, we invite the community to reflect on how this extraordinary backdrop could affect current ways to engineer and manage knowledge, including: what are limitations of generative AI systems and how can knowledge engineering be used to alleviate those? What are novel requirements for “high-quality” knowledge in neuro-symbolic architectures? What are emerging neuro-symbolic system patterns for performing knowledge engineering?
All submissions, including those related to the technologies mentioned on the special theme, should establish a clear connection to Knowledge Engineering and Knowledge Management or demonstrate a significant impact on the field. While acknowledging the interdisciplinary nature of knowledge and its interplay with other disciplines and technologies, such as Machine Learning, Natural Language Processing, and Computer Vision, contributions lacking direct relevance to Knowledge Engineering and Knowledge Management will not be considered pertinent to the EKAW conference.
Topics of interest
Knowledge Engineering
Methods, techniques, and tools for knowledge engineering
Evaluation methods and metrics for ensuring knowledge quality
Collaborative knowledge engineering
Ontology mapping and alignment
Ontology design patterns
Multimodal knowledge engineering
Methods for benchmarking/comparing Language Models for KE tasks
Uncertainty and vagueness in knowledge representation
Dealing with dynamic, distributed and emerging knowledge
Neuro-symbolic, GenAI and AI agent-based methodologies and architectures for knowledge engineering
Engineering of complex types of knowledge (e.g., causality, workflows, procedures)
(Ontological) knowledge memorization in LMs
Translating between explicitly represented (symbolic) knowledge and knowledge captured in machine learning models (parametric knowledge) or embeddings
Knowledge Management and Governance
Methods, techniques, and tools for knowledge management and governance
Knowledge evolution, maintenance, and preservation
Knowledge sharing and distribution
Methods for accelerating take-up of knowledge management technologies
Question answering over knowledge graphs via LMs
Robust and scalable knowledge management
Conversational AI and dialogue systems for knowledge management
Ethical and Trustworthy KE
Ethics, trust, and privacy in knowledge representation and reasoning
Explainable AI
Provenance, trust, and transparency in knowledge management
FAIR data and FAIR knowledge
Inclusivity and diversity in knowledge representation
Ontologies for trust and ethics
Policies for ownership, management and usage of knowledge
Social and Cognitive Aspects of KE
Knowledge representation inspired by cognitive science
Synergies between humans and machines
Knowledge emerging from user interaction and (social) networks
Knowledge ecosystems
Collaborative and social approaches to knowledge management and acquisition
Hybrid Humani-AI approaches to KE
Knowledge Discovery and Acquisition
Data and text mining for knowledge construction
Classification and clustering for knowledge management
Mining patterns and association rules
Formal Concept Analysis and extensions
Neuro-symbolic, GenAI and AI agent-based methodologies and architectures for knowledge discovery and acquisition
Knowledge graph extension, link prediction
Domain-specific Applications
eGovernment and public administration
Life sciences, health, and medicine
Humanities and Social Sciences
Cultural Heritage, Media and Digital Libraries
ICT4D (Knowledge in the developing world)
Manufacturing and automotive industry (Industry 4.0/5.0)
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