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SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration

Kan Ling, Zhen Qin, Yichi Zhu, Hengrun Zhang, Huiqun Yu, Guisheng Fan

TL;DR

SeDa is a unified framework for dataset discovery, semantic annotation, and multi-entity augmented navigation that employs a multi-entity augmented navigation strategy that organizes datasets within a knowledge space of sites, institutions, and enterprises, enabling contextual and provenance-aware exploration beyond traditional search paradigms.

Abstract

The continuous expansion of open data platforms and research repositories has led to a fragmented dataset ecosystem, posing significant challenges for cross-source data discovery and interpretation. To address these challenges, we introduce SeDa--a unified framework for dataset discovery, semantic annotation, and multi-entity augmented navigation. SeDa integrates more than 7.6 million datasets from over 200 platforms, spanning governmental, academic, and industrial domains. The framework first performs semantic extraction and standardization to harmonize heterogeneous metadata representations. On this basis, a topic-tagging mechanism constructs an extensible tag graph that supports thematic retrieval and cross-domain association, while a provenance assurance module embedded within the annotation process continuously validates dataset sources and monitors link availability to ensure reliability and traceability. Furthermore, SeDa employs a multi-entity augmented navigation strategy that organizes datasets within a knowledge space of sites, institutions, and enterprises, enabling contextual and provenance-aware exploration beyond traditional search paradigms. Comparative experiments with popular dataset search platforms, such as ChatPD and Google Dataset Search, demonstrate that SeDa achieves superior coverage, timeliness, and traceability. Taken together, SeDa establishes a foundation for trustworthy, semantically enriched, and globally scalable dataset exploration.

SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration

TL;DR

SeDa is a unified framework for dataset discovery, semantic annotation, and multi-entity augmented navigation that employs a multi-entity augmented navigation strategy that organizes datasets within a knowledge space of sites, institutions, and enterprises, enabling contextual and provenance-aware exploration beyond traditional search paradigms.

Abstract

The continuous expansion of open data platforms and research repositories has led to a fragmented dataset ecosystem, posing significant challenges for cross-source data discovery and interpretation. To address these challenges, we introduce SeDa--a unified framework for dataset discovery, semantic annotation, and multi-entity augmented navigation. SeDa integrates more than 7.6 million datasets from over 200 platforms, spanning governmental, academic, and industrial domains. The framework first performs semantic extraction and standardization to harmonize heterogeneous metadata representations. On this basis, a topic-tagging mechanism constructs an extensible tag graph that supports thematic retrieval and cross-domain association, while a provenance assurance module embedded within the annotation process continuously validates dataset sources and monitors link availability to ensure reliability and traceability. Furthermore, SeDa employs a multi-entity augmented navigation strategy that organizes datasets within a knowledge space of sites, institutions, and enterprises, enabling contextual and provenance-aware exploration beyond traditional search paradigms. Comparative experiments with popular dataset search platforms, such as ChatPD and Google Dataset Search, demonstrate that SeDa achieves superior coverage, timeliness, and traceability. Taken together, SeDa establishes a foundation for trustworthy, semantically enriched, and globally scalable dataset exploration.
Paper Structure (32 sections, 10 equations, 5 figures, 6 tables, 2 algorithms)

This paper contains 32 sections, 10 equations, 5 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Definition
  4. Dataset Search
  5. Data Navigation
  6. Data Annotation
  7. Schema Inference
  8. System Design
  9. Multi-source Data Integration
  10. Data Acquisition
  11. Data Processing
  12. Deduplication
  13. Topic Tagging and Provenance
  14. Topic Tag
  15. Dead Link Detection
  16. ...and 17 more sections

Figures (5)

  • Figure 1: Illustration of missing metadata elements in (a) Google Dataset Search and (b) ChatPD. Here, the dataset MS COCO has the URL, while Teapots and 3DShapes does not.
  • Figure 2: Overall architecture of the proposed system.
  • Figure 3: Tag graph construction.
  • Figure 4: Navigation module visualization.
  • Figure 5: Monthly comparison of newly found datasets and matches in ChatPD and GDS.