%0 Conference Paper %1 Breitinger_2020 %A Breitinger, Corinna %A Kolcu, Birkan %A Meuschke, Monique %A Meuschke, Norman %A Gipp, Bela %B Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 %D 2020 %I ACM %K academic-reference information-exploration jcdl2020 user-control %P 377-380 %R 10.1145/3383583.3398599 %T Supporting the Exploration of Semantic Features in Academic Literature using Graph-based Visualizations %U https://doi.org/10.1145%2F3383583.3398599 %X Literature search and recommendation systems have traditionally focused on improving recommendation accuracy through new algorithmic approaches. Less research has focused on the crucial task of visualizing the retrieved results to the user. Today, the most common visualization for literature search and recommendation systems remains the ranked list. However, this format exhibits several shortcomings, especially for academic literature. We present an alternative visual interface for exploring the results of an academic literature retrieval system using a force-directed graph layout. The interactive information visualization techniques we describe allow for a higher resolution search and discovery space tailored to the unique feature-based similarity present among academic literature. RecVis - the visual interface we propose - supports academics in exploring the scientific literature beyond textual similarity alone, since it enables the rapid identification of other forms of similarity, including the similarity of citations, figures, and mathematical expressions.

@inproceedings{Breitinger_2020, abstract = {Literature search and recommendation systems have traditionally focused on improving recommendation accuracy through new algorithmic approaches. Less research has focused on the crucial task of visualizing the retrieved results to the user. Today, the most common visualization for literature search and recommendation systems remains the ranked list. However, this format exhibits several shortcomings, especially for academic literature. We present an alternative visual interface for exploring the results of an academic literature retrieval system using a force-directed graph layout. The interactive information visualization techniques we describe allow for a higher resolution search and discovery space tailored to the unique feature-based similarity present among academic literature. RecVis - the visual interface we propose - supports academics in exploring the scientific literature beyond textual similarity alone, since it enables the rapid identification of other forms of similarity, including the similarity of citations, figures, and mathematical expressions. }, added-at = {2020-11-23T03:52:54.000+0100}, author = {Breitinger, Corinna and Kolcu, Birkan and Meuschke, Monique and Meuschke, Norman and Gipp, Bela}, biburl = {https://www.bibsonomy.org/bibtex/2ac64c602392c5953ddf1ec457ea314cf/brusilovsky}, booktitle = {Proceedings of the {ACM}/{IEEE} Joint Conference on Digital Libraries in 2020}, description = {Supporting the Exploration of Semantic Features in Academic Literature using Graph-based Visualizations | Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020}, doi = {10.1145/3383583.3398599}, interhash = {cb1d459384a3823932bf9dde83062b07}, intrahash = {ac64c602392c5953ddf1ec457ea314cf}, keywords = {academic-reference information-exploration jcdl2020 user-control}, month = aug, pages = {377-380}, publisher = {{ACM}}, timestamp = {2020-11-23T03:52:54.000+0100}, title = {Supporting the Exploration of Semantic Features in Academic Literature using Graph-based Visualizations}, url = {https://doi.org/10.1145%2F3383583.3398599}, year = 2020 }