SCOPE

Knowledge Discovery (KD) is an interdisciplinary domain focusing upon methodologies for identifying valid, hidden, novel, potentially actionable and meaningful information, from within data of all kinds. Knowledge discovery encompasses an end-to-end process involving: data preparation, the application of learning techniques and the presentation of the acquired knowledge in a manner that is both meaningful and (importantly) explainable. The learning techniques used range from statistically-based data mining, through sophisticated machine learning models to deep learning. Current trends in the field of KD include Explainable AI, Hybrid-learning, and the application of knowledge discovery to ever increasingly diverse data sets. Information Retrieval (IR), in turn, is concerned with the gathering of relevant information, from unstructured and semantically fuzzy data in texts and other media, typically in response to a user query. This encompasses searching for information within data sources (such as documents and images) and for metadata about those data sources, as well as searching within databases of all kinds and the Web. Automation of IR enables the reduction of information overload. The tools and techniques of KD are increasingly used to enhance and automate IR processes. Current trends in IR include learning to rank models, data representation using embedding techniques, and the use of knowledge graph technology. The scope of the KDIR conference covers all aspect KD and IR, and the overlap between the two.

CONFERENCE TOPICS

    Information Extraction
    Context Discovery
    Knowledge Discovery in Databases
    Applications of Knowledge Discovery and Information Retrieval
    Machine Learning
    Deep Learning
    Neural Networks
    Statistical Methods
    Data Analytics
    Large Language Models (LLMs)
    Generative AI
    Mining Text and Semi-Structured Data
    Pre-Processing and Post-Processing for Data Mining
    Data Processing and Exploratory Data Analysis
    Data Visualization
    Pattern Recognition
    Feature Selection
    Clustering and Classification Methods
    Natural Language Processing
    Interpretable and Explainable AI
    Knowledge Graphs and Ontologies