VizioMetrix: A Platform for Analyzing the Visual Information in Big Scholarly Data

WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web

Volumn , Issue , 2016, Pages 413-418

  Lee, Po Shen ^a   West, Jevin D ^a   Howe, Bill ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

bibliometrics; crowdsourcing; figure retrieval; information retrieval; open data; scientometrics; viziometrics

Indexed keywords

DATA MINING; DIGITAL STORAGE; INFORMATION RETRIEVAL; INFORMATION USE; OPEN DATA; WORLD WIDE WEB;

BIBLIOMETRIC; FIGURE RETRIEVAL; INFORMATION DISCOVERY; OPEN DATUM; RETRIEVAL APPLICATIONS; SCIENTIFIC LITERATURE; SCIENTOMETRICS; VISUAL ELEMENTS; VISUAL INFORMATION; VIZIOMETRIC;

CROWDSOURCING;

EID: 84977585408     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2872518.2890523     Document Type: Conference Paper

References (14)

1. A. Ahmed, E. P. Xing, W. W. Cohen, and R. F. Murphy. Structured correspondence topic models for mining captioned _gures in biological literature. InProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 39-48. ACM, 2009.
2. S. Bhatia, P. Mitra, and C. L. Giles. Finding algorithms in scientific articles. In Proceedings of the 19th international conference on World wide web, pages 1061-1062. ACM, 2010.
3. Z. Chen, M. Cafarella, and E. Adar. Diagramyer: A search engine for data-driven diagrams. In Proceedings of the 24th International Conference on World Wide Web Companion, WWW '15 Companion, pages 183-186, Republic and Canton of Geneva, Switzerland, 2015. International World Wide Web Conferences Steering Committee.
4. T. W. Fawcett and A. D. Higginson. Heavy use of equations impedes communication among biologists. Proceedings of the National Academy of Sciences, 109 (29): 11735-11739, 2012.
5. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
6. J. H. Larkin and H. A. Simon. Why a diagram is (sometimes) worth ten thousand words. Cognitive science, 11 (1): 65-100, 1987.
7. Y. LeCun, K. Kavukcuoglu, and C. Farabet. Convolutional networks and applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pages 253-256. IEEE, 2010.
8. P.-S. Lee, J. D., West, and B. Howe. Viziometrics: Analyzing visual information in the scientific literature. In Prep., 2016.
9. P.-s. Lee and B. Howe. Dismantling composite visualizations in the scientific literature. In International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, 2015.
10. S. Ray Choudhury and C. L. Giles. An architecture for information extraction from _gures in digital libraries. In Proceedings of the 24th International Conference on World Wide Web Companion, pages 667-672. International World Wide Web Conferences Steering Committee, 2015.
11. M. Rosvall and C. T. Bergstrom. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems. PLoS One, 6: e18209, 2011.
12. M. Savva, N. Kong, A. Chhajta, L. Fei-Fei, M. Agrawala, and J. Heer. ReVision: Automated Classification, Analysis and Redesign of Chart Images. In UIST '11, pages 393-402, 2011.
13. I. Wesley-Smith, R. Dandrea, and J. West. An experimental platform for scholarly article recommendation. In Proc. of the 2nd Workshop on Bibliometric-enhanced Information Retrieval (BIR2015), pages 30-39, 2015.
14. J. West, M., D. Vilhena, and C. Bergstrom. Ranking and mapping article-level citation networks. In Prep., 2016.