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ISEE: Interactive Semantic Enrichment for Database Fields

arXiv AI Archived Aug 05, 2026 ✓ Full text saved

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

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    Computer Science > Artificial Intelligence [Submitted on 21 Apr 2026] ISEE: Interactive Semantic Enrichment for Database Fields Yuan Tian, Yiru Chen, Rakesh R. Menon, Zifan Liu, Ting Cai, Fei Wu, Anudeep Chimakurthi, Prashanthi Ramamurthy, Sridevi Aishwariya Ganesan, Kun Qian, Yunyao Li 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. Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) Cite as: arXiv:2608.02604 [cs.AI]   (or arXiv:2608.02604v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2608.02604 Focus to learn more Submission history From: Yuan Tian [view email] [v1] Tue, 21 Apr 2026 22:28:25 UTC (2,227 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Aug 05, 2026
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    Aug 05, 2026
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