%0 Conference Paper %1 Shahaf:2012:MMS:2339530.2339706 %A Shahaf, Dafna %A Guestrin, Carlos %A Horvitz, Eric %B Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2012 %I ACM %K information-retrieval text- text-understanding visualisation %P 1122--1130 %R 10.1145/2339530.2339706 %T Metro maps of science %U http://doi.acm.org/10.1145/2339530.2339706 %X As the number of scientific publications soars, even the most enthusiastic reader can have trouble staying on top of the evolving literature. It is easy to focus on a narrow aspect of one's field and lose track of the big picture. Information overload is indeed a major challenge for scientists today, and is especially daunting for new investigators attempting to master a discipline and scientists who seek to cross disciplinary borders. In this paper, we propose metrics of influence, coverage and connectivity for scientific literature. We use these metrics to create structured summaries of information, which we call <i>metro maps</i>. Most importantly, metro maps explicitly show the relations between papers in a way which captures developments in the field. Pilot user studies demonstrate that our method helps researchers acquire new knowledge efficiently: map users achieved better precision and recall scores and found more seminal papers while performing fewer searches. %@ 978-1-4503-1462-6

@inproceedings{Shahaf:2012:MMS:2339530.2339706, abstract = {As the number of scientific publications soars, even the most enthusiastic reader can have trouble staying on top of the evolving literature. It is easy to focus on a narrow aspect of one's field and lose track of the big picture. Information overload is indeed a major challenge for scientists today, and is especially daunting for new investigators attempting to master a discipline and scientists who seek to cross disciplinary borders. In this paper, we propose metrics of influence, coverage and connectivity for scientific literature. We use these metrics to create structured summaries of information, which we call <i>metro maps</i>. Most importantly, metro maps explicitly show the relations between papers in a way which captures developments in the field. Pilot user studies demonstrate that our method helps researchers acquire new knowledge efficiently: map users achieved better precision and recall scores and found more seminal papers while performing fewer searches.}, acmid = {2339706}, added-at = {2013-01-09T10:31:54.000+0100}, address = {New York, NY, USA}, author = {Shahaf, Dafna and Guestrin, Carlos and Horvitz, Eric}, biburl = {https://www.bibsonomy.org/bibtex/288dbfc4fdb77ae1b1cacfe0262d6814e/gromgull}, booktitle = {Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining}, description = {Metro maps of science}, doi = {10.1145/2339530.2339706}, interhash = {62a27415491b5d5f0d9624447fab25d2}, intrahash = {88dbfc4fdb77ae1b1cacfe0262d6814e}, isbn = {978-1-4503-1462-6}, keywords = {information-retrieval text- text-understanding visualisation}, location = {Beijing, China}, numpages = {9}, pages = {1122--1130}, publisher = {ACM}, series = {KDD '12}, timestamp = {2013-01-09T10:31:54.000+0100}, title = {Metro maps of science}, url = {http://doi.acm.org/10.1145/2339530.2339706}, year = 2012 }