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

arXiv: https://arxiv.org/abs/2603.07502 · HTML: https://mlsanity.com/paper/2603.07502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)
- [Auctus: A Dataset Search Engine for Data Augmentation](https://mlsanity.com/paper/2102.05716/) (2021)
- [Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers](https://mlsanity.com/paper/2509.03059/) (2025)
- [ChatPD: An LLM-driven Paper-Dataset Networking System](https://mlsanity.com/paper/2505.22349/) (2025)
- [Lost or found? Discovering data needed for research](https://mlsanity.com/paper/1909.00464/) (2019)
- [AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability](https://mlsanity.com/paper/2507.12905/) (2025)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Everything you always wanted to know about a dataset: studies in data summarisation](https://mlsanity.com/paper/1810.12423/) (2018)
- [Evaluating Blocking Biases in Entity Matching](https://mlsanity.com/paper/2409.16410/) (2024)
- [AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials](https://mlsanity.com/paper/2506.11740/) (2025)

## Abstract (arXiv)

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.