Topics of Interest

We encourage submissions of high-quality research papers on the general areas of artificial intelligence, data science, databases, information retrieval, and knowledge management. Topics of interest include, but are not limited to, the following areas:

    Data and information acquisition and preprocessing (e.g., data crawling, IoT data, data quality, data privacy, mitigating biases, data wrangling)
    Integration and aggregation (e.g., semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, data lake, privacy and security, modeling, information credibility)
    Efficient data processing (e.g., serverless, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware)
    Special data processing (e.g., multilingual text, sequential, stream, time series, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data)
    Analytics and machine learning (e.g., OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection, and tracking, understanding, and interpretability)
    Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)
    Data preparation, Valuation, and Trading
    Information access and retrieval (e.g., web search, question answering and dialogue systems, retrieval models, query processing, personalization, recommender, and filtering systems)
    Users and interfaces for information systems (e.g., user behavior analysis, user interface design, perception of biases, personalization, interactive information retrieval, interactive analysis, spoken interfaces)
    Evaluation, performance studies, and benchmarks (e.g., online and offline evaluation, best practices)
    Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, best practices)
    Mining multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge graphs, and knowledge representations)
    Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)
    Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social media)
    Knowledge graphs support data representation and manipulation
    Generation of knowledge graphs using unstructured data 
    Information retrieval in the era of LLMs
    Open-ended QA systems
    Fairness, Accountability, Transparency, Ethics, and Explainability  in Information and Knowledge Management