Journal of Intelligent Information Systems

, Volume 39, Issue 1, pp 29–58 | Cite as

Semantic distances for technology landscape visualization

Article

Abstract

This paper presents a novel approach to the visualization of research domains in science and technology. The proposed methodology is based on the use of bibliometrics; i.e., analysis is conducted using information regarding trends and patterns of publication rather than the actual content. In particular, we explore the use of term co-occurrence frequencies as an indicator of semantic closeness between pairs of terms. To demonstrate the utility of this approach, a number of visualizations are generated for a collection of renewable energy related keywords. As these keywords are regarded as manifestations of the associated research topics, we contend that the proposed visualizations can be interpreted as representations of the underlying technology landscape.

Keywords

Data mining Technology forecasting Clustering Semantic distance 

Notes

Acknowledgements

We would like to thank the Masdar Institute of Science and Technology (MIST) and the Masdar Initiative for their support of this work.

References

  1. Antolín, G., Tinaut, F. V., Briceño, Y., Castaño, V., Pérez, C., & Ramírez, A. I. (2002). Optimisation of biodiesel production by sunflower oil transesterification. Bioresource Technology, 83(2), 111–114.CrossRefGoogle Scholar
  2. Anuradha, K., & Urs, S. (2007). Bibliometric indicators of indian research collaboration patterns: A correspondence analysis. Scientometrics, 71(2), 179–189.CrossRefGoogle Scholar
  3. Baek, N. C., Shin, U. C., & Yoon, J. H. (2005). A study on the design and analysis of a heat pump heating system using wastewater as a heat source. Solar Energy, 78(3), 427–440.CrossRefGoogle Scholar
  4. Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835–844.CrossRefGoogle Scholar
  5. Bishop, C. (1995). Neural networks for pattern recognition. London: Oxford University Press.Google Scholar
  6. Bishop, C. (2006). Pattern recognition and machine learning. Information science and statistics. Singapore: Springer.Google Scholar
  7. Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10(4), 57–67.CrossRefGoogle Scholar
  8. Braun, T., Schubert, A. P., & Kostoff, R. N. (2000). Growth and trends of fullerene research as reflected in its journal literature. Chemical Reviews, 100(1), 23–38.CrossRefGoogle Scholar
  9. Chiu, W.-T., & Ho, Y.-S. (2007). Bibliometric analysis of tsunami research. Scientometrics, 73(1), 3–17.CrossRefGoogle Scholar
  10. Cilibrasi, R., & Vitanyi, P. (2006). Automatic extraction of meaning from the web. In IEEE international symp. information theory.Google Scholar
  11. Cilibrasi, R. L., & Vitanyi, P. M. B. (2007). The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370–383.CrossRefGoogle Scholar
  12. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012.CrossRefGoogle Scholar
  13. Daim, T. U., Rueda, G. R., & Martin, H. T. (2005). Technology forecasting using bibliometric analysis and system dynamics. In Technology management: A unifying discipline for melting the boundaries (pp. 112–122).Google Scholar
  14. de Miranda, C., Dos, G. M., & Filho, L. F. (2006). Text mining as a valuable tool in foresight exercises: A study on nanotechnology. Technological Forecasting and Social Change, 73(8), 1013–1027.Google Scholar
  15. Ding, Y., Chowdhury, G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management, 37(6), 817–842.MATHCrossRefGoogle Scholar
  16. Elnekave, M. (2008). Adsorption heat pumps for providing coupled heating and cooling effects in olive oil mills. International Journal of Energy Research, 32(6), 559–568.CrossRefGoogle Scholar
  17. Glänzel, W., & Schubert, A. (2005). Analysing scientific networks through co-authorship. In Handbook of quantitative science and technology research (pp. 257–276).Google Scholar
  18. Hansel, A., & Lindblad, P. (1998). Towards optimization of cyanobacteria as biotechnologically relevant producers of molecular hydrogen, a clean and renewable energy source. Applied Microbiology and Biotechnology, 50(2), 153–160.CrossRefGoogle Scholar
  19. Igami, M. (2008). Exploration of the evolution of nanotechnology via mapping of patent applications. Scientometrics, 77(2), 289–308.CrossRefGoogle Scholar
  20. Janssens, F., Leta, J., Glänzel, W., & De Moor, B. (2006). Towards mapping library and information science. Information Processing & Management, 42(6), 1614–1642.CrossRefGoogle Scholar
  21. Kajikawa, Y., & Takeda, Y. (2008). Structure of research on biomass and bio-fuels: A citation-based approach. Technological Forecasting and Social Change, 75(9), 1349–1359.CrossRefGoogle Scholar
  22. Kajikawa, Y., Yoshikawa, J., Takeda, Y., & Matsushima, K. (2007). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75(6), 771–782.CrossRefGoogle Scholar
  23. Kim, M.-J. (2007). A bibliometric analysis of the effectiveness of Korea’s biotechnology stimulation plans, with a comparison with four other Asian nations. Scientometrics, 72(3), 371–388.CrossRefGoogle Scholar
  24. King, D. A. (2004). The scientific impact of nations. Nature, 430(6997), 311–316.CrossRefGoogle Scholar
  25. Kostoff, R. N. (2001). Text mining using database tomography and bibliometrics: A review. Technological Forecasting and Social Change, 68, 223–253.CrossRefGoogle Scholar
  26. Losiewicz, P., Oard, D., & Kostoff, R. (2000). Textual data mining to support science and technology management. Journal of Intelligent Information Systems, 15(2), 99–119.CrossRefGoogle Scholar
  27. Manning, C., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  28. Martino, J. (1993). Technological forecasting for decision making. McGraw-Hill Engineering and Technology Management Series.Google Scholar
  29. Mcdowall, W., & Eames, M. (2006). Forecasts, scenarios, visions, backcasts and roadmaps to the hydrogen economy: A review of the hydrogen futures literature. Energy Policy, 34(11), 1236–1250.CrossRefGoogle Scholar
  30. Morel, C., Serruya, S., Penna, G., & Guimarães, R. (2009). Co-authorship network analysis: A powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS Neglected Tropical Diseases, 3(8), e501.CrossRefGoogle Scholar
  31. Porter, A. (2005). Tech mining. Competitive Intelligence Magazine, 8(1), 30–36.Google Scholar
  32. Porter, A. (2007). How “tech mining” can enhance R&D management. Research Technology Management, 50(2), 15–20.Google Scholar
  33. Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.CrossRefGoogle Scholar
  34. Porter, A., Roper, A., Mason, T., Rossini, F., & Banks, J. (1991). Forecasting and management of technology. New York: Wiley-Interscience.Google Scholar
  35. Saitou, N., & Nei, M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406–425.Google Scholar
  36. Saka, A., & Igami, M. (2007). Mapping modern science using co-citation analysis. In IV ’07: Proceedings of the 11th international conference information visualization, Washington, DC, U.S.A. (pp. 453–458). Los Alamitos: IEEE Computer Society.Google Scholar
  37. Sammon, J. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 100(18), 401–409.CrossRefGoogle Scholar
  38. Smalheiser, N. R. (2001). Predicting emerging technologies with the aid of text-based data mining: The micro approach. Technovation, 21(10), 689–693.CrossRefGoogle Scholar
  39. Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics, 68(3), 595–610.CrossRefGoogle Scholar
  40. Takeda, Y., & Kajikawa, Y. (2009). Optics: A bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics, 78(3), 543–558.CrossRefGoogle Scholar
  41. Takeda, Y., Mae, S., Kajikawa, Y., & Matsushima, K. (2009). Nanobiotechnology as an emerging research domain from nanotechnology: A bibliometric approach. Scientometrics, 80(1), 23–38.CrossRefGoogle Scholar
  42. Upham, S., & Small, H. (2010). Emerging research fronts in science and technology: Patterns of new knowledge development. Scientometrics, 83(1), 15–38.CrossRefGoogle Scholar
  43. Van Der Heijden, K. (2000). Scenarios and forecasting—Two perspectives. Technological Forecasting and Social Change, 65, 31–36.CrossRefGoogle Scholar
  44. Woon, W., & Madnick, S. (2009). Asymmetric information distances for automated taxonomy construction. Knowledge and Information Systems, 21, 91–111. doi: 10.1007/s10115-009-0203-5.CrossRefGoogle Scholar
  45. Woon, W. L., Zeineldin, H., & Madnick, S. (2011). Bibliometric analysis of distributed generation. Technological Forecasting and Social Change, 78(3), 408–420.CrossRefGoogle Scholar
  46. Zhu, D., & Porter, A. (2002). Automated extraction and visualization of information for technological intelligence and forecasting. Technological Forecasting and Social Change, 69(5), 495–506.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Computing and Information Science ProgramMasdar Institute of Science and TechnologyAbu DhabiUnited Arab Emirates
  2. 2.Sloan School of ManagementMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations