BaleeGraph: Visualizing Co-Creation for Social Good

Authors

  • Kaira Sekiguchi The University of Tokyo
  • Yukio Ohsawa The University of Tokyo
  • Makoto Iida The University of Tokyo
  • Hisashi Nakamura The University of Tokyo

DOI:

https://doi.org/10.52731/liir.v004.170

Keywords:

Co-Creation, Visualization, Scenario Recommendation, BaleeGraph

Abstract

Addressing social challenges such as the Sustainable Development Goals (SDGs) necessitates the realization of co-creation, where diverse stakeholders have shared interests and complement each other. However, understanding the state of co-creation is challenging, which can render it less effective and manageable. To address this issue, we introduce a novel visualization method called Graph of BALloon tree with Embedding-based Edges (BaleeGraph) for recognizing the co-creation state. BaleeGraph operates on the assumption that each team possesses keywords and employs embedding vectors to calculate keyword similarity among different teams, thereby illustrating their relationships. We validate BaleeGraph's effectiveness through its application to a case study of Climate change actions with Co-creation powered by Regional weather information and E-technology (ClimCORE) project.
BaleeGraph enables participants to identify their strengths and shared interests with other teams. Furthermore, it uncovers paths from each team's keywords to those of the SDGs and analyzes time-series changes in the network structure. We conclude by discussing how BaleeGraph facilitates understanding co-creation for social good.

References

Ministry of Internal Affairs and Communications, “Sustainable develop-ment goals (SDGs),” https://www.soumu.go.jp/toukei toukatsu/index/kokusai/

K. Ueda, T. Takenaka, J. V´ancza, and L. Monostori, “Value creation and decision-making in sustainable society,” CIRP Annals, vol. 58, no. 2, pp. 681–700, 2009.

M. V. J. Veenman, B. H. A. M. Van Hout-Wolters, and P. Afflerbach, “Metacogni-tion and learning: Conceptual and methodological considerations,” Metacognition and Learning, no. 1, pp. 3–14, 2006.

ClimCORE Project Office, Research Center for Advanced Science and Technology, The University of Tokyo, “Website of Climate change actions with CO-creation pow-ered by Regional weather information and E-technology (ClimCORE), COI-NEXT open innovation,” https://climcore.rcast.u-tokyo.ac.jp/en/, 2023, Accessed: 2023/3.

H. Nakamura, K. Kuma, K. Onogi, T. Miyasaka, Y. Makihara, J. Ishida, and M. Iida, “Toward high-resolution regional atmospheric reanalysis for Japan: An overview of the ClimCORE project,” in 2022 IEEE International Conference on Big Data (Big Data), 2022, pp. 6153–6158.

Y. Ohsawa, N. Benson, and M. Yachida, “KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor,” in Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL’98-, 1998, pp. 12–18.

D. Iwasa, T. Hayashi, and Y. Ohsawa, “Development and evaluation of a new platform for accelerating cross-domain data exchange and cooperation,” New Generation Computing, vol. 38, no. 1, pp. 65–96, 2020. [Online]. Available: https://doi.org/10.1007/s00354-019-00080-0

O. Yukio, I. Takaichi, and M. I. Kamata, “Kamishibai KeyGraph: Tool for visualizing structural transitions for detecting transient causes,” New Mathematics and Natural Computation, vol. 06, no. 02, pp. 177–191, 07 2010.

S. Lozano, L. Calzada-Infante, A.-D. Belarmino, and S. Garc´ıa, “Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature,” Scien-tometrics, vol. 120, no. 2, pp. 609–629, 2019.

K. Sekiguchi and K. Hori, “Designing ethical artifacts has resulted in creative design: Empirical studies on the effect of an ethical design support tool,” AI & Society, vol. 36, pp. 101–148, 3 2021,

M. Sato, M. Akaishi, and K. Hori, “Topic bridging by identifying the dynamics of the spreading topic model,” in Intelligent Interactive Multimedia: Systems and Services,

T. Watanabe, J. Watada, N. Takahashi, R. J. Howlett, and L. C. Jain, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 619–627.

F. Windhager, L. Zenk, and P. Federico, “Visual enterprise network analytics - visual-izing organizational change,” Procedia - Social and Behavioral Sciences, vol. 22, pp. 59–68, 12 2011.

M. Ishihara, “A visualization method for the growth process and difference of dy-namic networks,” Master’s Thesis, Graduate School of Systems and Information En-gineering, University of Tsukuba, March 2007, (in Japanese; Title was translated by authors).

K. Tanaka and K. Hori, “Finding diachronic objects of drifting descriptions by sim-ilar mentions,” in Knowledge Management and Acquisition for Intelligent Systems,

K. Ohara and Q. Bai, Eds. Cham: Springer International Publishing, 2019, pp. 32–43.

R. Abumalloh, M. Nilashi, M. Yousoof, A. Alhargan, A. Alghamdi, A. Alzahrani,

L. Saraireh, R. Osman, and S. Asadi, “Medical image processing and covid-19: A literature review and bibliometric analysis,” Journal of Infection and Public Health, vol. 15, 11 2021.

G. Cernile, T. Heritage, N. J. Sebire, B. Gordon, T. Schwering, S. Kazemlou, and

Y. Borecki, “Network graph representation of covid-19 scientific publications to aid knowledge discovery,” BMJ Health & Care Informatics, vol. 28, no. 1, 2021.

S.-H. Lim, J. Chae, G. Cong, D. Herrmannova, R. M. Patton, R. Kannan, and T. E. Potok, “Visual understanding of covid-19 knowledge graph for predictive analysis,” in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 4381–4386.

OpenAI, “OpenAI API,” https://platform.openai.com/overview, 2020.

N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Nat-ural Language Processing. Association for Computational Linguistics, 11 2019.

——, “Making monolingual sentence embeddings multilingual using knowledge dis-tillation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2020.

NetworkX Developers, “Software for complex networks,” https://networkx.org/, 2014.

A. Hagberg, P. Swart, and D. S Chult, “Exploring network structure, dynamics, and function using NetworkX,” in Proceedings of the 7th Python in Science Conference (SciPy2008), G. Varoquaux, T. Vaught, and J. Millman, Eds., 2008, pp. 11–15.

Pyvis Developers, “Pyvis: A python library for visualizing networks,” https://pyvis.readthedocs.io/en/latest/, 2016.

M. Ishihara, “A visualization method for the growth process and difference of dy-namic networks,” Master’s Thesis, Graduate School of Systems and Information En-gineering, University of Tsukuba, March 2007, (in Japanese; Title was translated by authors).

K. Tanaka and K. Hori, “Finding diachronic objects of drifting descriptions by sim-ilar mentions,” in Knowledge Management and Acquisition for Intelligent Systems,

K. Ohara and Q. Bai, Eds. Cham: Springer International Publishing, 2019, pp. 32–43.

R. Abumalloh, M. Nilashi, M. Yousoof, A. Alhargan, A. Alghamdi, A. Alzahrani,

L. Saraireh, R. Osman, and S. Asadi, “Medical image processing and covid-19: A literature review and bibliometric analysis,” Journal of Infection and Public Health, vol. 15, 11 2021.

G. Cernile, T. Heritage, N. J. Sebire, B. Gordon, T. Schwering, S. Kazemlou, and

Y. Borecki, “Network graph representation of covid-19 scientific publications to aid knowledge discovery,” BMJ Health & Care Informatics, vol. 28, no. 1, 2021.

S.-H. Lim, J. Chae, G. Cong, D. Herrmannova, R. M. Patton, R. Kannan, and T. E. Potok, “Visual understanding of covid-19 knowledge graph for predictive analysis,” in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 4381–4386.

OpenAI, “OpenAI API,” https://platform.openai.com/overview, 2020.

N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Nat-ural Language Processing. Association for Computational Linguistics, 11 2019.

——, “Making monolingual sentence embeddings multilingual using knowledge dis-tillation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2020.

NetworkX Developers, “Software for complex networks,” https://networkx.org/, 2014.

A. Hagberg, P. Swart, and D. S Chult, “Exploring network structure, dynamics, and function using NetworkX,” in Proceedings of the 7th Python in Science Conference (SciPy2008), G. Varoquaux, T. Vaught, and J. Millman, Eds., 2008, pp. 11–15.

Pyvis Developers, “Pyvis: A python library for visualizing networks,” https://pyvis.readthedocs.io/en/latest/, 2016.

M. Ishihara, “A visualization method for the growth process and difference of dy-namic networks,” Master’s Thesis, Graduate School of Systems and Information En-gineering, University of Tsukuba, March 2007, (in Japanese; Title was translated by authors).

K. Tanaka and K. Hori, “Finding diachronic objects of drifting descriptions by sim-ilar mentions,” in Knowledge Management and Acquisition for Intelligent Systems,

K. Ohara and Q. Bai, Eds. Cham: Springer International Publishing, 2019, pp. 32–43.

R. Abumalloh, M. Nilashi, M. Yousoof, A. Alhargan, A. Alghamdi, A. Alzahrani,

L. Saraireh, R. Osman, and S. Asadi, “Medical image processing and covid-19: A literature review and bibliometric analysis,” Journal of Infection and Public Health, vol. 15, 11 2021.

G. Cernile, T. Heritage, N. J. Sebire, B. Gordon, T. Schwering, S. Kazemlou, and

Y. Borecki, “Network graph representation of covid-19 scientific publications to aid knowledge discovery,” BMJ Health & Care Informatics, vol. 28, no. 1, 2021.

S.-H. Lim, J. Chae, G. Cong, D. Herrmannova, R. M. Patton, R. Kannan, and T. E. Potok, “Visual understanding of covid-19 knowledge graph for predictive analysis,” in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 4381–4386.

OpenAI, “OpenAI API,” https://platform.openai.com/overview, 2020.

N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Nat-ural Language Processing. Association for Computational Linguistics, 11 2019.

——, “Making monolingual sentence embeddings multilingual using knowledge dis-tillation,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2020.

NetworkX Developers, “Software for complex networks,” https://networkx.org/, 2014.

A. Hagberg, P. Swart, and D. S Chult, “Exploring network structure, dynamics, and function using NetworkX,” in Proceedings of the 7th Python in Science Conference (SciPy2008), G. Varoquaux, T. Vaught, and J. Millman, Eds., 2008, pp. 11–15.

Pyvis Developers, “Pyvis: A python library for visualizing networks,” https://pyvis.readthedocs.io/en/latest/, 2016.

Stockholm Resilience Center, “The sdgs wedding cake,” https://www.stockholmresilience.org/research/research-news/2016-06-14-the-sdgs-wedding-cake.html, Accessed: July 30, 2023.

A. Shimpo, K. Takemura, S. Wakamatsu, H. Togawa, Y. Mochizuki, M. Takekawa, S. Tanaka, K. Yamashita, S. Maeda, R. Kurora, H. Murai, N. Kitabatake, H. Tsuguti, H. Mukougawa, T. Iwasaki, R. Kawamura, M. Kimoto, I. Takayabu, Y. N. Takayabu, Y. Tanimoto, T. Hirooka, Y. Masumoto, M. Watanabe, K. Tsuboki, and H. Nakamura, “Primary factors behind the heavy rain event of July 2018 and the subsequent heat wave in Japan,” SOLA, vol. 15A, pp. 13–18, 2019.

T. Horinouchi, Y. Kosaka, H. Nakamigawa, H. Nakamura, N. Fujikawa, and Y. N. Takayabu, “Moisture supply, jet, and silk-road wave train associated with the pro-longed heavy rainfall in Kyushu, Japan in early July 2020,” SOLA, vol. 17B, no. Spe-cial Edition, pp. 1–8, 2021.

Downloads

Published

2023-12-20