JACIII Vol.14 No.2 pp. 150-154
doi: 10.20965/jaciii.2010.p0150


Analysis and Visualization of Japanese Law Networks Based on Granular Computing -Visual Law: Visualization System of Japanese Law-

Tetsuya Toyota and Hajime Nobuhara

Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenoudai, Tsukuba Science City, Ibaraki 305-8573, Japan

June 26, 2009
July 29, 2009
March 20, 2010
granular computing, visualization, network science, law engineering, hierarchical structure
In order to grasp a perspective of the over 7,000 laws in Japan, and to find the relationships between law and laws, a method of creating a hierarchical network of laws using granular computing, is proposed. The proposed method analyze hierarchical networks by using an index of network science such as degree distribution and closeness centrality. Furthermore, it visualizes the hierarchical structure within the setting of granular computing. Using the JAVA-based language ‘Prefuse,’ a law network visualization system ‘Visual Law’ is implemented, and it is confirmed that users can easily analyze/understand the law network structure using our proposal.
Cite this article as:
T. Toyota and H. Nobuhara, “Analysis and Visualization of Japanese Law Networks Based on Granular Computing -Visual Law: Visualization System of Japanese Law-,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.2, pp. 150-154, 2010.
Data files:
  1. [1] A. Bargiela and W. Pedrycz, “Granular Computing An Introduction,” Kluwer Academic Pablishers, 2002.
  2. [2] T. Theodosiou, N. Darzentas, L. Angelis, and C. A. Ouzounis, “PuRed-MCL : a graph-based PubMed document clustering methodology,” Bioinformatics, Vol.24, No.17, 2008.
  3. [3] B. W. Chee and B. Schatz, “Document Clustering using Small World Communities,” Proc. of the 2007 Conf. on Digital libraries, pp. 53-62, 2007.
  4. [4] J. L. Neto, A. D. Santos, C. A. A. Kaestner, and A. A. Freitas, “Document Clustering and Text Summarization,” Proc. 4th Int. Conf. Practical Applications of Knowledge Discovery and Data Mining, pp. 41-55, 2000.
  5. [5] Prefuse
  6. [6] K. Hirota, “Development of Fuzzy Legal Expert System for CISG,” First Tunisian-Japanese Seminar on Science and Technology, April 2000. (Tunis, Tunisia)
  7. [7] H. Yoshino, “Legal Knowledge Based System and Legal Education - Focusing on understanding Change of Legal Relation -,” in: Pompeu Casanova(ed.), Trend on Legal knowledge, the semantic Web and the regulation of electronic Social Systems. Instituto de Investigaciones Juridicas UNAM, Mexico. (in publishing 2006/2007)
  8. [8] Y. Jin, Y. Matsuo, and M. Ishizuka, “Extracting Inter-Firm Networks from World Wide Web Using General-Purpose Search Engine,” J. of Online Information Review, Vol.32, No.2, 2008.
  9. [9] T. Suzuki, T. Ikeguchi, and Y. Horiom, “Estimating Structures of Complex Networks Hidden in Nikkei 225 Stock Market,” IEICE technical report. Nonlinear problems Vol.105, No.547, pp. 135-140, 2006.
  10. [10] Japan’s e-Government Initiatives
  11. [11] Pajek
  12. [12] ChaSen
  13. [13] N. Masuda and N. Konno, “Introduction to complex networks,” Sangyo-tosyo, 2005. (in Japanese)
  14. [14] D. J. Watts, “Small Worlds: The Dynamics of Networks Between Order and Randomness,” Princeton Univ. Press, 1999.
  15. [15] L. Freeman, “Centrality in social networks: Conceptual clarification,” Social Networks Vol.1, No.3, pp. 215-239, 1979.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024