# Towards More Usable Dataset Search: From Query Characterization to Snippet Generation

arXiv: https://arxiv.org/abs/1908.11146 · HTML: https://mlsanity.com/paper/1908.11146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions](https://mlsanity.com/paper/2402.12001/) (2024)
- [Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding](https://mlsanity.com/paper/2209.11067/) (2022)

## Abstract (arXiv)

Reusing published datasets on the Web is of great interest to researchers and developers. Their data needs may be met by submitting queries to a dataset search engine to retrieve relevant datasets. In this ongoing work towards developing a more usable dataset search engine, we characterize real data needs by annotating the semantics of 1,947 queries using a novel fine-grained scheme, to provide implications for enhancing dataset search. Based on the findings, we present a query-centered framework for dataset search, and explore the implementation of snippet generation and evaluate it with a preliminary user study.