International Conference on Knowledge Engineering and Knowledge Management

概要

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) Contribution Types EKAW-25 distinguishes between research, in-use, and vision papers. The papers will all have the same status and follow the same formatting guidelines in the proceedings but will receive special treatment during the reviewing phase. Research papers: These are standard papers presenting a novel method, technique, or analysis with appropriate empirical or evaluation. In-use papers: These are papers describing knowledge management and engineering applications in real environments. Vision papers: Along the lines of this year’s special theme, we invite papers discussing how major technological advances will impact our field within the next 10+ years.

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