KOREAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE AND LAW
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KAAIL · AI LAW & POLICY RESEARCH

Semantic Data Governance and Collaborative Architecture for LLM-Based Natural Language Analytics in Urban Rail Transit

Abstract-based AI summary: The authors identify fragmented data and inconsistent terminology and metric definitions across different urban rail transit operating systems as obstacles to LLM-based natural language analytics. They propose semantic data governance and a collaborative analytics architecture combining a lakehouse, a semantic layer, and a unified metric store, with domain rules and execution validation…

International ResearchCrossrefAI summary
Author

Shiqi Xiong · Guodong Liu

Publication DateLanguage und

AI Research Summary

Before Human Review

Abstract-based · Full paper not reviewed · Not an official association position

Summary created

The authors identify fragmented data and inconsistent terminology and metric definitions across urban rail transit systems as obstacles to LLM-based natural language analytics. They propose a semantic data governance and collaborative analytics architecture combining a lakehouse, a semantic layer and a unified metric store, with domain rules and execution validation to control query generation.

Key Points from the Abstract

  1. The proposed data foundation organizes physical schemas, business terminology and statistical definitions.
  2. Domain rules, network topology and a controlled execution workflow support query generation and cross-domain analysis.
  3. Two scenarios, power-supply fault assistance and passenger-flow–traction-energy analysis, illustrate how the components work together.

Relevance to AI Law and Policy

The study concerns consistent management of data terminology and metrics, and control of query generation and execution for LLM-based urban rail transit analytics.

Limitations to Consider

  • This summary is based on the abstract; the full paper was not reviewed.
  • The contribution is an engineering architecture and semantic governance mechanisms, rather than a comparative evaluation of general-purpose LLM algorithms.
  • The abstract provides no quantitative performance improvements or validation of effects in actual operations.

Please compare with the original text before citing or using it in your assessment.

Abstract Preview
Urban rail transit data are distributed across heterogeneous operational systems, where inconsistent semantics and fragm…
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Research Topic

AI Governance

Original text and source

Original Title

Semantic Data Governance and Collaborative Architecture for LLM-Based Natural Language Analytics in Urban Rail Transit

The Korean title on this page was translated by AI.
Journal or publication
Frontiers in Computing and Intelligent Systems
Original publisher
Darcy & Roy Press Co. Ltd.
Database
Crossref
Original paperRead more in the original text
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