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

2026-03-08

AI Summary

This work addresses the severe fragmentation of open data platforms, which significantly impedes cross-source data discovery and semantic interoperability. To overcome this challenge, the authors propose a unified framework that integrates over 7.6 million datasets through semantic extraction and normalization, a scalable topic-tag graph, multi-entity–enhanced navigation, and a provenance-aware mechanism. The resulting system establishes a context-aware and traceable paradigm for data exploration. Compared to existing platforms such as ChatPD and Google Dataset Search, the proposed approach demonstrates substantial improvements in data coverage breadth, timeliness, and provenance tracking, thereby enabling more efficient and semantically rich cross-domain data discovery.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Search and Optimization: Distributed Search
• Natural Language Processing: Information Extraction

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Web Mining and Content Analysis: Web data integration and cleaning

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.

Problem

• dataset discovery
• fragmented dataset ecosystem
• cross-source data discovery
• semantic exploration
• data interoperability

Innovation

• semantic annotation
• multi-entity navigation
• dataset discovery
• provenance assurance
• tag graph