Concept maps for designing adaptive knowledge maps

Information Visualization

Volumn 5, Issue 3, 2006, Pages 211-224

  Crampes, Michel ^a   Ranwez, Sylvie ^a   Villerd, Jean ^a   Velickovski, Filip ^a   Mooney, Chris ^b   Emery, Andrew ^b   Mille, Nicholas ^c  

^a ECOLE DES MINES D ALÈS   (France)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c NETIA Inc   (France)

Author keywords

Adaptivity; Concept maps; Knowledge maps; Multidimensional scaling; Music interface; Navigation

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER AIDED DESIGN; KNOWLEDGE ACQUISITION; NAVIGATION; PROFESSIONAL ASPECTS; SOFTWARE ENGINEERING; USER INTERFACES; VISUALIZATION;

ADAPTIVITY; CONCEPT MAPS; KNOWLEDGE MAPS; MULTIDIMENSIONAL SCALING; MUSIC INTERFACE;

KNOWLEDGE ENGINEERING;

EID: 33748492786     PISSN: 14738716     EISSN: 14738724     Source Type: Journal    
DOI: 10.1057/palgrave.ivs.9500127     Document Type: Article

References (26)

1. Cañas A, Carff R, Hill G, Carvalho M, Arguedas M, Eskridge TC, Lott J, Carvajal R. Concept maps: integrating knowledge and information visualization. In: Tergan S-O and Keller T (Eds). Knowledge and Information Visualization: Searching for Synergies. Springer Lecture Notes in Computer Science: Heidelberg/New York, 2005; 205-220.
2. Cañas J, Hill G, Carff R, Suri N, Lott J, Eskridge T, Gómez G, Arroyo M, Carvajal R. Cmaptools: a knowledge modelling and sharing environment. In: First International Conference on Concept Mapping 2004 (Pamplona, Spain, 2004), Universidad Pública de Navarra, Pamplona, Spain. 2004; 125-133.
3. Novak JD, Cañas AJ. The theory underlying concept maps and how to construct them [WWW document] http://cmap.ihmc.us/Publications/ResearchPapers/ TheoryUnderlyingConceptMaps.pdf (accessed 13 April 2006).
4. Battista G, Eades P, Tamassia R, Tollis I. Graph Drawing. Algorithms for the Visualisation of Graphs. Prentice Hall: Upper Saddle River, New Jersey, 1999.
5. Herman I, Melancon G, Marshall MS. Graph visualization and navigation in Information visualization: a survey. IEEE Transactions on Visualization and Computer Graphics 2000; 6: 24-43.
6. Ware C, Bobrow R. Supporting visual queries on medium-sized node-link diagrams. Information Visualization 2005; 4: 49-58.
7. Vinh Nguyen Q, Huang V. EncCon: an approach to constructing interactive visualization of large hierarchical data. Information Visualization 2005; 4: 1-21.
8. Chalmers M. A linear iteration time layout algorithm for visualising high-dimensional data. IEEE Symposium on Information Visualization 1996. (San Francisco, USA, 1996) IEEE Computer Society Press: Chicago, 1996; 127-132.
9. Wong N, Carpendale S, Greenberg S. EdgeLens: an interactive method for managing edge congestion in graphs. IEEE Symposium on Information Visualization 2003 (Seattle, USA, 2003). IEEE Computer Society Press: Chicago, 2003; 51-58.
10. Shneiderman B. The eyes have it: a task by data type taxonomy for information visualizations. IEEE Symposium on Visual Languages. (Las Alamos, USA, 1996) IEEE Computer Society Press: Chicago, 1996; 336-343.
11. Fluit C, Sabou M, van Harmelen E Ontology-based information visualization. In: Geroimenko V and Chen C (Eds). Visualising the Semantic Web. Springer-Verlag: London, 2002;
12. Le Grand B, Soto M. Visualisation of the semantic web topic maps visualisation. IEEE Information Visualization Conference 2002 (London, UK, 2002) IEEE Computer Society Press: Chicago, 2002; 344.
13. Fekete JD. The InfoVis toolkit. IEEE Symposium on Information Visualization 2004 (Austin, USA, 2004). IEEE Computer Society Press: Chicago, 2006; 167-174.
14. Buja A, Swayne DF. Visualization methodology for MDS. Journal of Classification 2002; 19: 7-43.
15. W. Basalaj, Proximity visualizalisation of abstract data. Technical Report UCAM-CL-TR-509, University of Cambridge, 2000.
16. Heer J, Card S, Landaym J. Prefuse: a toolkit for interactive information visualization. Human Factors in Computing Systems, Proceedings of the SIGCHI Conference (Portland, Oregon), 2005, 421-430.
17. Card S-K, Mackinlay J. The structure of the information visualization design space. IEEE Symposium on Information Visualization 1997 (Phoenix, USA, 1997). IEEE Computer Society Press: Chicago, 1997; 92-99.
18. Chi EH. A taxonomy of visualization techniques using the data state reference model. IEEE Symposium on Information Visualization 2000 (Salt Lake City, USA, 2000). IEEE Computer Society Press: Chicago, 2000; 69-75.
19. Goldberg A. Smalltalk-80: The Interactive Programming Environment, Addison-Wesley: Boston, MA, USA, 1984, 516pp.
20. Crampes M, Ranwez S, Velickovski F, Mooney C, Mille M. An integrated visual approach for music indexing and dynamic playlist composition. 13th Annual Multimedia Computing and Networking (San Jose, USA, 2006), IF&T SPIE, Bellingham WA, USA.
21. Platt JC. FastMap, MetricMap and Landmark MDS are all Nyström algorithms. 10th International Workshop on Artificial Intelligence and Statistics. The Savannah Hotel, Barbados. Society for Artificial Intelligence and Statistics: New Jersey, 2005; 261-268.
22. Rodden K, Basalaj W, Sinclair D, Wood K. Does Organisation by Similarity Assist Image Browsing. ACM Press: 2001;
23. Eades P. A heuristic for graph drawing. 13th Manitoba Conference on Numerical Mathematics and Computing 1984 (Winnipeg, Canada, 1984), Utilitas Mathematica Publishing: Winnipeg.
24. Eades P, Lin X. Spring algorithms and symmetry. Theoretical Computer Science 2000; 240: 379-405.
25. Morrison A, Ross G, Chalmers M. Fast multidimensional scaling through sampling, springs and interpolation. Information Visualization 2003; 2: 68-77.
26. Furnas GW. Generalized fisheye views. In: Mantel M and Orbeton P (Eds). Human Factors in Computing Systems 1986. ACM Press: New York, 1986; 16-23.