Knowledge Discovery (KD) is an interdisciplinary domain focusing upon methodologies for identifying valid, hidden, novel, potentially useful 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. The learning techniques used range from statistically-based data mining, through sophisticated machine learning to deep learning facilitated but recent increases in computer processing power. 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 documents and for metadata about documents, as well as searching relational databases and the Web. Automation of IR enables the reduction of what has been called “information overload”. The tools and techniques of KD are increasingly used to enhance and automate information retrieval 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.

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