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DataHunter: A System for Finding Datasets Based on Scientific Problem Descriptions

DataHunter: A System for Finding Datasets Based on Scientific Problem Descriptions

Published: 2021

Buchtitel: Proceedings of the 15th ACM Recommender Systems Conference (RecSys'21)
Seiten: 1-5
Verlag: ACM

Referierte Veröffentlichung


The number of datasets is steadily rising, making it increasingly difficult for researchers and practitioners in the various scientific disciplines to be aware of all datasets, particularly of the most relevant datasets for a given research problem. To this end, dataset search engines have been proposed. However, they are based on the users' keywords and thus have difficulties in determining precisely fitting datasets for complex research problems. In this paper, we propose the system at that recommends suitable datasets to users based on given research problem descriptions. It is based on fastText for the text representation and text classification, the Data Set Knowledge Graph (DSKG) with metadata about almost 1,700 unique datasets, as well as 88,000 paper abstracts as research problem descriptions for training the model. Overall, our system demonstrates that recommending datasets facilitates data provisioning and reuse according to the FAIR principles and that dataset recommendation is a promising future research direction.

Download: Media:DataHunter_RecSys2021.pdf
DOI Link: 10.1145/3460231.3478882

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Web Science


Information Retrieval, Natürliche Sprachverarbeitung, Digitale Bibliotheken, Knowledge Discovery, Künstliche Intelligenz