A Collaborative Support for Recommending References in Papers
Resumo
Understanding citations to scientific publications is a task of vital importance in the academic world. This task can be supported by appropriate data structures and visualization mechanisms. One challenge is the amount of existing relationships and the difficulty of determining which of the references of a document are considered the most potentially relevant to it. In this paper, we propose a visual approach based on graphs to recommend important references. Also, we propose a procedure to build and update the citation graph in a collaborative way.
Referências
M. C. Waumans and H. Bersini, “Genealogical trees of scientific papers,” PloS one, vol. 11, no. 3, p. e0150588, 2016. https://doi.org/10.1371/journal.pone.0150588
H. Wei, Y. Zhao, S. Wu, Z. Deng, F. Parvinzamir, F. Dong, E. Liu, and G. Clapworthy, “Management of scientific documents and visualization of citation relationships using weighted key scientific terms.” in DATA, 2016, pp. 135–143. https://doi.org/10.5220/0005981501350143
S. A. Greenberg, “How citation distortions create unfounded authority: analysis of a citation network,” Bmj, vol. 339, p. b2680, 2009. https://doi.org/10.1136/bmj.b2680
D. Gough, S. Oliver, and J. Thomas, An introduction to systematic reviews. Sage, 2017.
U. Schäfer and U. Kasterka, “Scientific authoring support: A tool to navigate in typed citation graphs,” in Proceedings of the NAACL HLT 2010 workshop on computational linguistics and writing: Writing processes and authoring aids. Association for Computational Linguistics, 2010, pp. 7–14.
M. Valenzuela, V. Ha, and O. Etzioni, “Identifying meaningful citations.” in AAAI Workshop: Scholarly Big Data, 2015.
X. Zhu, P. Turney, D. Lemire, and A. Vellino, “Measuring academic influence: Not all citations are equal,” Journal of the Association for Information Science and Technology, vol. 66, no. 2, pp. 408–427, 2015. https://doi.org/10.1002/asi.23179
R. M. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen, “Joint latent topic models for text and citations,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008, pp. 542–550. https://doi.org/10.1145/1401890.1401957
N. Crouch and M. P. David, “Pyscholargraph: A graph-based framework for indexing, searching and visualising relationships between academic papers,” The ANU Undergraduate Research Journal, vol. 161, 2015.
D. E. Ciriello, “Five hundred deep learning papers, graphviz and python.” http://dnlcrl.github.io/projects/2015/10/10/500-deep-learning-papers-graphviz-python, 2015.
G. Salton and C.-S. Yang, “On the specification of term values in automatic indexing,” Journal of documentation, vol. 29, no. 4, pp. 351–372, 1973.
M. Berger, K. McDonough, and L. Seversky, “cite2vec: Citation-driven document exploration via word embeddings,” IEEE Transactions on Visualization & Computer Graphics, no. 1, pp. 1–1, 2017. https://doi.org/10.1109/TVCG.2016.2598667
G. Ginde, “Visualisation of massive data from scholarly article and journal database a novel scheme,” arXiv preprint arXiv:1611.01152,2016.
A. Vukotic, N. Watt, T. Abedrabbo, D. Fox, and J. Partner, Neo4j in action. Manning Publications Co., 2014.
Y. Sibaroni, D. H. Widyantoro, and M. L. Khodra, “Survey on research paper’s relations,” in Information Technology Systems and Innovation (ICITSI), 2015 International Conference on. IEEE, 2015, pp. 1–6. https://doi.org/10.1109/ICITSI.2015.7437741
A. P. Singh, K. Shubhankar, and V. Pudi, “An efficient algorithm for ranking research papers based on citation network,” in Data Mining and Optimization (DMO), 2011 3rd Conference on. IEEE, 2011, pp. 88–95. https://doi.org/10.1109/DMO.2011.5976510
L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999.
Z. Zhou, C. Shi, M. Hu, and Y. Liu, “Visual ranking of academic influence via paper citation,” Journal of Visual Languages & Computing, vol. 48, pp. 134–143, 2018. https://doi.org/10.1016/j.jvlc.2018.08.007
G. Packer, “Cheap words,” The New Yorker, vol. 17, 2014.
T. S. Newman and H. Yi, “A survey of the marching cubes algorithm,” Computers & Graphics, vol. 30, no. 5, pp. 854–879, 2006. https://doi.org/10.1016/j.cag.2006.07.021
G. Van Rossum and F. L. Drake, The python language reference manual. Network Theory Ltd., 2011.
D. Framework, “Django the web framework for perfectionists with deadlines,” https:// docs.djangoproject.com/ en/ 2.0/ , vol. 1, 2016.
B. Almende, “vis. js–a dynamic, browser based visualization library,” http:// visjs.org/ , vol. 1, 2016.
H. Wei, Y. Zhao, S. Wu, Z. Deng, F. Parvinzamir, F. Dong, E. Liu, and G. Clapworthy, “Management of scientific documents and visualization of citation relationships using weighted key scientific terms.” in DATA, 2016, pp. 135–143. https://doi.org/10.5220/0005981501350143
S. A. Greenberg, “How citation distortions create unfounded authority: analysis of a citation network,” Bmj, vol. 339, p. b2680, 2009. https://doi.org/10.1136/bmj.b2680
D. Gough, S. Oliver, and J. Thomas, An introduction to systematic reviews. Sage, 2017.
U. Schäfer and U. Kasterka, “Scientific authoring support: A tool to navigate in typed citation graphs,” in Proceedings of the NAACL HLT 2010 workshop on computational linguistics and writing: Writing processes and authoring aids. Association for Computational Linguistics, 2010, pp. 7–14.
M. Valenzuela, V. Ha, and O. Etzioni, “Identifying meaningful citations.” in AAAI Workshop: Scholarly Big Data, 2015.
X. Zhu, P. Turney, D. Lemire, and A. Vellino, “Measuring academic influence: Not all citations are equal,” Journal of the Association for Information Science and Technology, vol. 66, no. 2, pp. 408–427, 2015. https://doi.org/10.1002/asi.23179
R. M. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen, “Joint latent topic models for text and citations,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008, pp. 542–550. https://doi.org/10.1145/1401890.1401957
N. Crouch and M. P. David, “Pyscholargraph: A graph-based framework for indexing, searching and visualising relationships between academic papers,” The ANU Undergraduate Research Journal, vol. 161, 2015.
D. E. Ciriello, “Five hundred deep learning papers, graphviz and python.” http://dnlcrl.github.io/projects/2015/10/10/500-deep-learning-papers-graphviz-python, 2015.
G. Salton and C.-S. Yang, “On the specification of term values in automatic indexing,” Journal of documentation, vol. 29, no. 4, pp. 351–372, 1973.
M. Berger, K. McDonough, and L. Seversky, “cite2vec: Citation-driven document exploration via word embeddings,” IEEE Transactions on Visualization & Computer Graphics, no. 1, pp. 1–1, 2017. https://doi.org/10.1109/TVCG.2016.2598667
G. Ginde, “Visualisation of massive data from scholarly article and journal database a novel scheme,” arXiv preprint arXiv:1611.01152,2016.
A. Vukotic, N. Watt, T. Abedrabbo, D. Fox, and J. Partner, Neo4j in action. Manning Publications Co., 2014.
Y. Sibaroni, D. H. Widyantoro, and M. L. Khodra, “Survey on research paper’s relations,” in Information Technology Systems and Innovation (ICITSI), 2015 International Conference on. IEEE, 2015, pp. 1–6. https://doi.org/10.1109/ICITSI.2015.7437741
A. P. Singh, K. Shubhankar, and V. Pudi, “An efficient algorithm for ranking research papers based on citation network,” in Data Mining and Optimization (DMO), 2011 3rd Conference on. IEEE, 2011, pp. 88–95. https://doi.org/10.1109/DMO.2011.5976510
L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999.
Z. Zhou, C. Shi, M. Hu, and Y. Liu, “Visual ranking of academic influence via paper citation,” Journal of Visual Languages & Computing, vol. 48, pp. 134–143, 2018. https://doi.org/10.1016/j.jvlc.2018.08.007
G. Packer, “Cheap words,” The New Yorker, vol. 17, 2014.
T. S. Newman and H. Yi, “A survey of the marching cubes algorithm,” Computers & Graphics, vol. 30, no. 5, pp. 854–879, 2006. https://doi.org/10.1016/j.cag.2006.07.021
G. Van Rossum and F. L. Drake, The python language reference manual. Network Theory Ltd., 2011.
D. Framework, “Django the web framework for perfectionists with deadlines,” https:// docs.djangoproject.com/ en/ 2.0/ , vol. 1, 2016.
B. Almende, “vis. js–a dynamic, browser based visualization library,” http:// visjs.org/ , vol. 1, 2016.
Publicado
28/10/2019
Como Citar
GUILARTE, Orlando Fonseca; BARBOSA, Simone Diniz Junqueira; PESCO, Sinesio.
A Collaborative Support for Recommending References in Papers. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 42-48.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8300.
