All aspects of AI, machine learning, knowledge acquisition, data engineering and management for intelligent systems, including (but not restricted to):

    Knowledge Acquisition
        Fundamental views on knowledge that affect the knowledge acquisition process and the use of knowledge in knowledge engineering
        Algorithmic approaches to knowledge acquisition
        Tools and techniques for knowledge acquisition, knowledge maintenance and knowledge validation
        Evaluation of knowledge acquisition techniques, tools and methods.
        Ontology and its role in knowledge acquisition
        Knowledge acquisition applications tested and deployed in real-life settings
    Knowledge Representation and Discovering
        Knowledge representation learning
        Temporal knowledge graph
        Data linkage
        Data analytics and mining
        Big data acquisition and analysis
        Machine learning/deep learning
        Semantic Web, the Linked Data and the Web of Data
    Responsible Data/Knowledge Management and System
        Transparency, explainability, trust, and accountability
        Privacy and security
        Other ethical concerns
    Knowledge-aware Application
        Question answering
        Recommendation system
        Domain-related application
    Human-centric Knowledge Engineering
        Human-machine collaboration, integration, interaction, delegation, dialog
        Hybrid approaches combining knowledge engineering and machine learning
    Other Topics
        Experience and Lesson learned
        Reproducibility and negative results of knowledge engineering
        Innovative user interfaces
        Crowd-sourcing for data generation and problem solving