Computer-supported Interactive Assignment of Keywords for Literature Collections

In the proceedings of IEEE VIS Workshop on Machine Learning from User Interaction for Visualization and Analytics, 2018

Abstract:

A curated literature collection on a specific topic helps researchers to find relevant articles quickly. Assigning multiple keywords to each article is one of the techniques to structure such a collection. But it is challenging to assign all the keywords consistently without any gaps or ambiguities. We propose to support the user with a machine learning technique that suggests keywords for articles in a literature collection browser. We provide visual explanations to make the keyword suggestions transparent. The suggestions are based on previous keyword assignments. The machine learning technique learns on the fly from the interactive assignments of the user. We seamlessly integrate the proposed technique in an existing literature collection browser and investigate various usage scenarios through an early prototype.

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To cite: Shivam Agarwal, Jürgen Bernard, Fabian Beck, "Computer-supported Interactive Assignment of Keywords for Literature Collections" In the proceedings of IEEE VIS Workshop on Machine Learning from User Interaction for Visualization and Analytics, 2018.

BibTeX:

@inproceedings{Agarwal2018Computer,
author = {Agarwal, Shivam and Bernard, Jürgen and Beck, Fabian},
title = {Computer-supported Interactive Assignment of Keywords for Literature Collections},
booktitle = {IEEE VIS Workshop on Machine Learning from User Interaction for Visualization and Analytics},
abstract = {A curated literature collection on a specific topic helps researchers to find relevant articles quickly. Assigning multiple keywords to each article is one of the techniques to structure such a collection. But it is challenging to assign all the keywords consistently without any gaps or ambiguities. We propose to support the user with a machine learning technique that suggests keywords for articles in a literature collection browser. We provide visual explanations to make the keyword suggestions transparent. The suggestions are based on previous keyword assignments. The machine learning technique learns on the fly from the interactive assignments of the user. We seamlessly integrate the proposed technique in an existing literature collection browser and investigate various usage scenarios through an early prototype.},
year = {2018}
}