ISEE: Interactive Semantic Enrichment for Database Fields

Researchers introduced ISEE, an interactive semantic enrichment system that uses LLM-based agents to improve database field descriptions by scoring their quality, gathering domain knowledge, and collaborating with users. In evaluations including user studies and automated simulations, ISEE reduced cognitive load, improved description quality, and enhanced downstream task performance such as entity-linking.

arXiv:2608.02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval. However, their performance heavily depends on the clarity and completeness of data semantics. In practice, many field descriptions remain ambiguous or incomplete, as much of the essential context e.g., the meaning of a customized field originates from users' domain knowledge and is rarely documented publicly. This gap restricts the agents' task performance in downstream tasks, such as entity-linking. To bridge this gap, we introduce a novel and comprehensive Interactive SEmantic Enrichment system ISEE . Given a data field description, ISEE measures its quality through a scoring system, gathers domain knowledge, and collaboratively enriches the semantics with users. Through a user study, automated user simulation, quantitative evaluation, and case study, we demonstrate that ISEE significantly reduces cognitive load, improves description quality, and enhances downstream task performance.