Información de la conferencia

KEOD 2026: International Conference on Knowledge Engineering and Ontology Development

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KEOD
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Día de Entrega:
2026-07-03 Extended
Fecha de Notificación:
2026-07-17
Fecha de conferencia:
2026-10-28
Ubicación:
Angers, France
Ediciones:
18
ICORE: C   Vistas: 18720   Seguidores: 0   Asistentes: 0

Índice Conference Partner (CP-I)

52,6 / 100
Puesto n.º 1.057 de 5.650 congresos · 19% superior

N.º 53 de 253 en Sistemas de información y Web N.º 94 de 734 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%)
58
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
79
Atención de la comunidad (10%)
12
Integridad del registro público (15%)
35

Datos utilizados: Categorías: ICORE C · Ediciones documentadas: 18 · Investigadores que abrieron esta página en los últimos 24 meses: 4

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Confianza 80 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-04

Solicitud de Artículos

KEOD 2026 (International Conference on Knowledge Engineering and Ontology Development) is a ICORE C conference held in Angers, France on 2026-10-28. The paper submission deadline is 2026-07-03 (extended). Acceptance notifications are sent on 2026-07-17.

SCOPE Knowledge Engineering (KE) refers to the technical, scientific, and social aspects involved in building, maintaining, and utilizing knowledge-based systems. As a multidisciplinary field, KE draws upon methodologies from artificial intelligence (AI), databases, expert systems, decision support systems, and information systems, with strong ties to software engineering principles. KE also intersects with disciplines like logic, cognitive science, and socio-cognitive engineering. In recent years, the integration of Large Language Models (LLMs) has opened new pathways in ontology development, enabling automated extraction, refinement, and evolution of ontologies. Additionally, the rise of Low-code and No-code Platforms empowers non-experts to participate in ontology engineering, broadening accessibility and fostering innovation. Ontology Development (OD) focuses on building reusable semantic structures such as vocabularies, glossaries, and formal ontologies that specify types of entities and relationships within a domain. These semantic structures are increasingly central to applications like knowledge graphs, digital twins, explainable AI (XAI), and cybersecurity frameworks, where ontologies enhance data integration, decision-making, and system transparency. Current applications of KE and OD include sustainable AI solutions, semantic interoperability in IoT, natural language processing (NLP), and enterprise engineering. Ontologies now play a crucial role in ensuring ethical AI development by mitigating bias and enhancing transparency. The KEOD conference aims to be a major meeting point for researchers and practitioners interested in methodologies and technologies related to Knowledge Engineering and Ontology Development. It encourages the exploration of cutting-edge topics such as LLM-based Ontology Development, Ontology-driven Digital Twins, and Ontology-enhanced Low-code Platforms, fostering dialogue and innovation across academic and industrial spheres. CONFERENCE TOPICS Knowledge Engineering Ontology Engineering Knowledge Acquisition Knowledge Representation Ontologies and Knowledge Graphs Domain Ontologies Ontology Tools Ontology Quality Assurance Ontology Sharing and Reuse Ontology Matching and Alignment Integration and Interoperability LLM-based Ontology Development Semantic Web Ontologies in Low-code and No-code Platforms Natural Language Processing Automated Ontology Learning and Evolution Explainable Artificial Intelligence (XAI) in Ontology Development Applications and Case-Studies Ontologies in Industry Domain Analysis and Modeling Enterprise Engineering Enterprise Ontology Knowledge Graphs and Graph Neural Networks (GNNs) Ontology-driven Digital Twins Reference Models Semantic Interoperability in IoT and Cyber-Physical Systems
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