Recommending Scientific Datasets Using Author Networks in Ensemble Methods

Tracking #: 720-1700


Responsible editor: 

Stephen Pettifer

Submission Type: 

Research Paper

Abstract: 

Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation: the task of recommending relevant datasets given a dataset that is already known to be relevant. Previous work has used meta-data descriptions of datasets and interest profiles of authors to support dataset recommendation. In this work, we are the first to investigate the use of co-author networks to drive the recommendation of relevant datasets. We also investigate the combination of such co-author networks with existing methods, resulting in three different algorithms for dataset recommendation. We obtain experimental results on a realistic corpus which show that only the ensemble combination of all three algorithms achieves sufficiently high precision for the dataset recommendation task.

Manuscript: 

Tags: 

  • Reviewed

Data repository URLs: 

The data, python implementation code, and sample experiment could be found at this link.

Some of the RDF/HDT datasets could be found at this link.

Date of Submission: 

Tuesday, February 22, 2022

Date of Decision: 

Friday, April 22, 2022

Decision: 

Accept

Solicited Reviews: