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 acquisition and processing (e.g., IoT Data, Data Quality, Data Privacy, Mitigating Biases, Data Wrangling, Data exploration, Data Preparation, Valuation, and Trading)

Data Integration and aggregation (e.g., Semantic Processing, data provenance, data linkage, data fusion, knowledge graph, 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, algorithmic interpretability)

Neural Information and knowledge processing (e.g., graph neural networks, domain adaptation, transfer learning, network architectures, neural ranking, neural recommendation, and neural prediction)

Information access and retrieval (e.g., Information Retrieval with Large Language Models, Question Answering and Dialogue Systems, Open-ended Question Answering Systems, Generation of Knowledge Graphs from Unstructured data, Retrieval Models, Query Processing, Personalization, Recommender Systems, Filtering Systems)

Users and interfaces for information systems (e.g., user behavior analysis, user interface design, perception of biases, interactive information retrieval, interactive analysis, spoken interfaces)

Evaluation (e.g., performance studies, benchmarks, online and offline evaluation, best practices)

Crowdsourcing (e.g. task assignment, worker reliability, optimization, trustworthiness, transparency, crowdsourcing in the era of large language models)
Mining multi-modal content (e.g., natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge representations)

Data presentation (e.g., visualization, summarization, readability, VR, speech input/output)

Fairness, Ethics, and Explainability in Information and Knowledge Management (e.g., fairness, accountability, ethics, explainability)

Applications (e.g., urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social networks, education, business)

Generative AI for Data and Knowledge Management (e.g., GenAI for structured and unstructured data processing, GenAI for data synthesis and simulation, GenAI for information summarization, content creation, and visualization)

 Resource Efficient Generative AI models for knowledge management (e.g., model compression, distributed learning, and leveraging edge computing to reduce computational overhead.)