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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