Supporting Human-Centric Data Exploration Through Semantics and Natural Language Interaction

Data science plays an increasingly central role in decision-making across domains, yet the effectiveness of these decisions hinges not only on sophisticated algorithms but also on how well systems support human interpretation, exploration, and communication of data. This tutorial explores the intersection of semantics, natural language processing (NLP), and human-computer interaction in creating human-centric data exploration tools that promote accessibility, trust, and transparency. This includes techniques for generating perceptually meaningful visual encodings, using NLP for query interpretation and ambiguity resolution, and designing conversational interfaces that align with users' intent. The tutorial will highlight research from a broad set of contributors in the SIGMOD/VLDB, HCI, and visualization communities, spanning natural language interfaces for databases, multimodal interaction systems, semantic search for data repositories, and the use of AI and large language models to augment visual and textual analysis.