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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