Visual Analysis and Knowledge Discovery for Text

  • Christin Seifert
  • Vedran Sabol
  • Wolfgang Kienreich
  • Elisabeth Lex
  • Michael Granitzer


Providing means for effectively accessing and exploring large textual data sets is a problem attracting the attention of text mining and information visualization experts alike. The rapid growth of the data volume and heterogeneity, as well as the richness of metadata and the dynamic nature of text repositories, add to the complexity of the task. This chapter provides an overview of data visualization methods for gaining insight into large, heterogeneous, dynamic textual data sets. We argue that visual analysis, in combination with automatic knowledge discovery methods, provides several advantages. Besides introducing human knowledge and visual pattern recognition into the analytical process, it provides the possibility to improve the performance of automatic methods through user feedback.


Word Sense Disambiguation Information Visualization Semantic Integration Semantic Enrichment Information Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Andrews, K., Kienreich, W., Sabol, V., Becker, J., Droschl, G., Kappe, F., Granitzer, M., Auer, P., Tochtermann, K.: The infoSky visual explorer: exploiting hierarchical structure and document similarities. Inf. Vis. 1(3–4), 166–181 (2002)CrossRefGoogle Scholar
  2. 2.
    Bleiholder, J., Naumann, F.: Data fusion. ACM Comput. Surv. 41, 1:1–1:41 (2009)Google Scholar
  3. 3.
    Bruijn, J.d., Ehrig, M., Feier, C., Martìns-Recuerda, F., Scharffe, F., Weiten, M.: Ontology Mediation, Merging, and Aligning, in Semantic Web Technologies: Trends and Research in Ontology-based Systems (eds J. Davies, R. Studer and P. Warren), John Wiley & Sons, Ltd, Chichester, UK. pp. 95–113. (2006). doi:10.1002/047003033X.ch6Google Scholar
  4. 4.
    Cao, N., Sun, J., Lin, Y.R., Gotz, D., Liu, S., Qu, H.: Facetatlas: multifaceted visualization for rich text corpora. IEEE Trans. Vis. Comput. Graph. 16(6), 1172–1181 (2010)CrossRefGoogle Scholar
  5. 5.
    Das, D., Martins, A.F.: A survey on automatic text summarization. Technical report, Carnegie Mellon University (2007). Literature Survey for the Language and Statistics II course at CMUGoogle Scholar
  6. 6.
    Díaz, J., Petit, J., Serna, M.: A survey of graph layout problems. ACM Comput. Surv. 34, 313–356 (2002)CrossRefGoogle Scholar
  7. 7.
    Dykes, J., MacEachren, A.M., Kraak, M.J. (eds.): Exploring Geovisualization. Elsevier, Amsterdam (2005)Google Scholar
  8. 8.
    Eppler, M.J., Burkhard, R.A.: Knowledge visualization. In: Schwartz, D. & D. Te’eni (eds.) Encyclopedia of Knowledge Management, Second Edition, PA: Information Science Reference. pp. 987–999. Hershey. doi:10.4018/978-1-59904-931-1.ch094Google Scholar
  9. 9.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996)Google Scholar
  10. 10.
    Fluit, C.: Autofocus: semantic search for the desktop. Inf. Vis. Int. Conf. 0, 480–487 (2005)Google Scholar
  11. 11.
    Fodor, I.: A survey of dimension reduction techniques. Technical report UCRL-ID-148494, US DOE Office of Scientific and Technical Information (2002)Google Scholar
  12. 12.
    Gantz, J.F., Reinsel, D., Chute, C., Schlichting, W., McArthur, J., Minton, S., Xheneti, I., Toncheva, A., Manfrediz, A.: The expanding digital universe, a forecast of worldwide information growth through 2010. IDC White Paper – sponsored by EMC (2007)Google Scholar
  13. 13.
    Gantz, J.F., Chute, C., Manfrediz, A., Minton, S., Reinsel, D., Schlichting, W., Toncheva, A.: The diverse and exploding digital universe, an updated forecast of worldwide information growth through 2011. IDC White Paper – sponsored by EMC (2008)Google Scholar
  14. 14.
    Granitzer, M.: Adaptive term weighting through stochastic optimization. In: Gelbukh, A. (ed.) Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, vol. 6008, pp. 614–626. Springer, Berlin/Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Granitzer, M., Neidhart, T., Lux, M.: Learning term spaces based on visual feedback. In: International Workshop on Database and Expert Systems Applications (DEXA), Krakow, pp. 176–180. IEEE Computer Society (2006)Google Scholar
  16. 16.
    Granitzer, M., Sabol, V., Onn, K.W., Lukose, D., Tochtermann, K.: Ontology alignment – a survey with focus on visually supported semi-automatic techniques. Future Internet 2(3), 238–258 (2010)CrossRefGoogle Scholar
  17. 17.
    Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: visualizing thematic changes in large document collections. IEEE Trans. Vis. Comput. Graph. 8(1), 9–20 (2002)CrossRefGoogle Scholar
  18. 18.
    Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: A survey. IEEE Trans. Vis. Comput. Graph. 6, 24–43 (2000)CrossRefGoogle Scholar
  19. 19.
    Inselberg, A., Dimsdale, B.: Parallel coordinates for visualizing multi-dimensional geometry. In: CG International ’87 on Computer Graphics 1987. Springer-Verlag New York, Inc., Karuizawa, Japan, New York, NY, USA, pp. 25–44 (1987).
  20. 20.
    Kaiser, K., Miksch, S.: Information extraction – a survey. Technical report Asgaard-TR-2005-6, Vienna University of Technology (2005)Google Scholar
  21. 21.
    Kandlhofer, M.: Einbindung neuer Visualisierungskomponenten in ein Multiple Coordinated Views Framework, Endbericht Master-Praktikum (2008)Google Scholar
  22. 22.
    Kapler, T., Wright, W.: Geo time information visualization. Inf. Vis. 4, 136–146 (2005)CrossRefGoogle Scholar
  23. 23.
    Keim, D.A., Mansmann, F., Oelke, D., Ziegler, H.: Visual analytics: combining automated discovery with interactive visualizations. In: Discovery Science, LNAI, Springer Berlin/ Heidelberg, Budapest, Hungary, pp. 2–14 (2008)Google Scholar
  24. 24.
    Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual analytics: scope and challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining, pp. 76–90. Springer, Berlin/Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Kienreich, W., Seifert, C.: An application of edge bundling techniques to the visualization of media analysis results. In: Proceedings of the International Conference on Information Visualization, London. IEEE Computer Society Press (2010)Google Scholar
  26. 26.
    Kienreich, W., Zechner, M., Sabol, V.: Comprehensive astronomical visualization for a multimedia encyclopedia. In: International Symposium of Knowledge and Argument Visualization; Proceedings of the International Conference Information Visualisation, Zurich, pp. 363–368. IEEE Computer Society (2007)Google Scholar
  27. 27.
    Krishnan, M., Bohn, S., Cowley, W., Crow, V., Nieplocha, J.: Scalable visual analytics of massive textual datasets. In: IEEE International Parallel and Distributed Processing Symposium, 2007. IPDPS 2007, Long Beach, pp. 1–10 (2007)Google Scholar
  28. 28.
    Lex, E., Seifert, C., Kienreich, W., Granitzer, M.: A generic framework for visualizing the news article domain and its application to real-world data. J. Digit. Inf. Manag. 6, 434–441 (2008)Google Scholar
  29. 29.
    Muhr, M., Kern, R., Granitzer, M.: Analysis of structural relationships for hierarchical cluster labeling. In: Proceedings of the International ACM Conference on Research and Development in Information Retrieval (SIGIR), SIGIR ’10, Geneva, pp. 178–185. ACM, New York (2010)Google Scholar
  30. 30.
    Muhr, M., Sabol, V., Granitzer, M.: Scalable recursive top-down hierarchical clustering approach with implicit model selection for textual data sets. In: IEEE International Workshop on Text-Based Information Retrieval; Proceedings of the International Conference on Database and Expert Systems Applications, Bilbao (2010)Google Scholar
  31. 31.
    Müller, F.: Granularity based multiple coordinated views to improve the information seeking process. Ph.D. thesis, University of Konstanz, Germany (2005)Google Scholar
  32. 32.
    Muthukrishnan, P., Radev, D., Mei, Q.: Edge weight regularization over multiple graphs for similarity learning. In: IEEE 10th International Conference on Data Mining (ICDM), 2010, Sydney, pp. 374–383 (2010). doi:10.1109/ICDM.2010.156Google Scholar
  33. 33.
    Rennison, E.: Galaxy of news: an approach to visualizing and understanding expansive news landscapes. In: Proceedings of the ACM Symposium on User Interface Software and Technology, UIST ’94, Marina del Rey, pp. 3–12. ACM, New York (1994)Google Scholar
  34. 34.
    Ribeiro-Neto, B., Baeza-Yates, R.: Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd edn. Pearson Education, Ltd., Harlow, England, Addison-Wesley (2011).
  35. 35.
    Risch, J.S., Rex, D.B., Dowson, S.T., Walters, T.B., May, R.A., Moon, B.D.: The STARLIGHT information visualization system. Readings in Information Visualization, pp. 551–560. Morgan Kaufmann, San Francisco (1999)Google Scholar
  36. 36.
    Saaty, T.L.: Principia Mathematica Decernendi: Mathematical Principles of Decision Making, 1st edn. RWS Publications, Pittsburgh, PA, USA (2010)Google Scholar
  37. 37.
    Sabol, V., Kienreich, W., Muhr, M., Klieber, W., Granitzer, M.: Visual knowledge discovery in dynamic enterprise text repositories. In: Proceedings of the International Conference Information Visualisation (IV), pp. 361–368. IEEE Computer Society, Washington, DC (2009)Google Scholar
  38. 38.
    Sabol, V., Syed, K., Scharl, A., Muhr, M., Hubmann-Haidvogel, A.: Incremental computation of information landscapes for dynamic web interfaces. In: Proceedings of the Brazilian Symposium on Human Factors in Computer Systems, Barcelona, Belo Horizonte, Brazil pp. 205–208 (2010).
  39. 39.
    Scharl, A., Tochtermann, K.: The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society (Advanced Information and Knowledge Processing). Springer, New York/Secaucus (2007)Google Scholar
  40. 40.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  41. 41.
    Seifert, C., Granitzer, M.: User-based active learning. In: Fan, W., Hsu, W., Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) Proceedings of the International Conference on Data Mining Workshops (ICDM), Sydney, pp. 418–425 (2010)Google Scholar
  42. 42.
    Seifert, C., Lex, E.: A novel visualization approach for data-mining-related classification. In: Proceedings if the International Conference on Information Visualisation (IV), Barcelona, pp. 490–495. Wiley (2009)Google Scholar
  43. 43.
    Seifert, C., Lex, E.: A visualization to investigate and give feedback to classifiers. In: Proceedings of the European Conference on Visualization (EuroVis), Berlin (2009). PosterGoogle Scholar
  44. 44.
    Seifert, C., Kump, B., Kienreich, W., Granitzer, G., Granitzer, M.: On the beauty and usability of tag clouds. In: Proceedings of the International Conference on Information Visualisation (IV), London, pp. 17–25. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  45. 45.
    Seifert, C., Sabol, V., Granitzer, M.: Classifier hypothesis generation using visual analysis methods. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds.) Networked Digital Technologies. Communications in Computer and Information Science, vol. 87, pp. 98–111. Springer, Berlin/Heidelberg (2010)Google Scholar
  46. 46.
    Seifert, C., Kienreich, W., Granitzer, M.: Visualizing text classification models with Voronoi word clouds. In: Proceedings of the International Conference Information Visualisation (IV), London (2011). PosterGoogle Scholar
  47. 47.
    Shalev-Shwartz, S., Singer, Y., Ng, A.Y.: Online and batch learning of pseudo-metrics. In: International Conference on Machine learning (ICML), Banff, p. 94 (2004)Google Scholar
  48. 48.
    Shneiderman, B.: Inventing discovery tools: combining information visualization with data mining. Inf. Vis. 1(1), 5–12 (2002)Google Scholar
  49. 49.
    Shneiderman, B., Plaisant, C.: Designing the User Interface: Strategies for Effective Human-Computer Interaction, 5th edn. Addison-Wesley Publ. Co., Reading, MA, p. 606 (2010)Google Scholar
  50. 50.
    Thomas, J.J., Cook, K.A. (eds.): Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  51. 51.
    Tochtermann, K., Sabol, V., Kienreich, W., Granitzer, M., Becker, J.: Enhancing environmental search engines with information landscapes. In: International Symposium on Environmental Software Systems, Semmering. (2003)
  52. 52.
    Tukey, J.W.: Exploratory Data Analysis, 1st edn. Addison Wesley, Massachusetts (1977)MATHGoogle Scholar
  53. 53.
    van Ham, F., Wattenberg, M., Viegas, F.B.: Mapping text with phrase nets. IEEE Trans. Vis. Comput. Graph. 15, 1169–1176 (2009)CrossRefGoogle Scholar
  54. 54.
    Weber, M., Alexa, M., Muller, W.: Visualizing time-series on spirals. In: IEEE Symposium on Information Visualization, 2001. INFOVIS 2001, San Diego, pp. 7–13 (2001)Google Scholar
  55. 55.
    Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Christin Seifert
    • 1
  • Vedran Sabol
    • 2
  • Wolfgang Kienreich
    • 2
  • Elisabeth Lex
    • 2
  • Michael Granitzer
    • 1
  1. 1.University of PassauPassauGermany
  2. 2.Know-Center GrazGrazAustria

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