Visualising Computational Intelligence through Converting Data into Formal Concepts

  • Simon Andrews
  • Constantinos Orphanides
  • Simon Polovina
Part of the Studies in Computational Intelligence book series (SCI, volume 352)


Formal Concept Analysis (FCA) is an emerging data technology that complements collective intelligence such as that identified in the Semantic Web, by visualising the hidden meaning in disparate and distributed data. The chapter demonstrates the discovery of these novel semantics through a set of FCA open source software tools, FcaBedrock and In − Close, that were developed by the authors. These tools add computational intelligence by converting data into a Boolean form called a Formal Context, prepare this data for analysis by creating focused and manageable sub-contexts and then analyse the prepared data using a visualisation called a Concept Lattice. The Formal Concepts thus visualised highlight how data itself contains meaning, and how FCA tools thereby extract data’s inherent semantics. The chapter describes how this will be further developed in a project called “Combining and Uniting Business Intelligence with Semantic Technologies” (CUBIST), to provide in-data-warehouse visual analytics for Resource Description Framework (RDF)-based triple stores.


Formal Concept Analysis (FCA) Formal Context Formal Concept visualisation Concept Lattice data warehousing in-warehouse analytics objects and attributes Galois connection Semantic Web RDF distributed data disparate data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andrews, S.: In-Close, A Fast Algorithm for Computing Formal Concepts (2009),
  2. 2.
    Andrews, S.: Data conversion and interoperability for FCA. In: Conceptual Structures Tools Interoperability Workshop, 17th International Conference on Conceptual Structures (ICCS 2009), Moscow (2009),
  3. 3.
    Andrews, S.: In-Close (2010),
  4. 4.
    Andrews, S., Orphanides, C.: FcaBedrock, a Formal Context Creator. In: Croitoru, M., Ferré, S., Lukose, D. (eds.) ICCS 2010. LNCS, vol. 6208, pp. 181–184. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Andrews, S., Orphanides, C.: FcaBedrock, a Formal Context Creator (2010),
  6. 6.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), Google Scholar
  7. 7.
    Becker, P., Correia, J.H.: The ToscanaJ Suite for Implementing Conceptual Information Systems. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 324–348. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Becker, P., Correia, J.H.: ToscanaJ (2005),
  9. 9.
    Berners-Lee, T.: Why RDF model is different from the XML model (1998),
  10. 10.
    Frequent Itemset Mining Implementations Repository,
  11. 11.
    The Friend of a Friend (FOAF) project,
  12. 12.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin (1998); Translated by C. FranzkeGoogle Scholar
  13. 13.
    Goethals, B., Zaki, M.: Advances in Frequent Itemset Mining Implementations: Report on FIMI 2003. SIGKDD Explorations Newsletter 6(1), 109–117 (2004)CrossRefGoogle Scholar
  14. 14.
    Harris, S., Gibbins, N.: 3store: Efficient bulk RDF storage. In: Proceedings of the 1st International Workshop on Practical and Scalable Semantic Web Systems (PSSS) 2003, pp. 1–15 (2003),
  15. 15.
    Horrocks, I., Patel-Schneider, P.F., Van Harmelen, F.: From SHIQ and RDF to OWL: the making of a Web Ontology Language. Web Semantics: Science, Services and Agents on the World Wide Web 1(1), 7–26 (2003), Scholar
  16. 16.
    Imberman, S., Domanski, B.: Finding Association Rules from Quantitative Data using Data Booleanization (1999),
  17. 17.
    Jin, R., Breitbart, Y., Muoh, C.: Data discretization unification. Knowledge and Information Systems 19(1), 1–29 (2009)CrossRefGoogle Scholar
  18. 18.
    Krajca, P., Outrata, J., Vychodil, V.: Parallel Recursive Algorithm for FCA. In: Belohlavek, R., Kuznetsov, S.O. (eds.) Proceeding of the Sixth International Conference on Concept Lattices and their Applications, pp. 71–82. Palacky University, Olomouc (2008)Google Scholar
  19. 19.
    Kaytoue-Uberall, M., Duplesssis, S., Napoli, A.: Using Formal concept Analysis for the Extraction of Groups of Co-expressed Genes. In: Le Thi, H.A., Bouvry, P., Pham Dinh, T. (eds.) MCO 2008. CCIS, vol. 14, pp. 439–449. Springer, Heidelberg (2008)Google Scholar
  20. 20.
    Passin, T.B.: Explorer’s Guide to the Semantic Web. Manning, Greenwich (2004)Google Scholar
  21. 21.
    Priss, U.: Formal Concept Analysis in Information Science. In: Cronin, B. (ed.) Annual Review of Information Science and Technology. ASIST, vol. 40 (2008)Google Scholar
  22. 22.
    Priss, U.: FcaStone - FCA File Format and Interoperability Software. In: Croitoru, M., Jaschkë, R., Rudolph, S. (eds.) Conceptual Structures and the Web, Proceedings of the Third Conceptual Structures and Tool Interoperability Workshop, pp. 33–43 (2008)Google Scholar
  23. 23.
    Priss, U.: FCA Software Interoperability. In: Belohlavek, R., Kuznetsov, S.O. (eds.) Proceeding of the Sixth International Conference on Concept Lattices and Their Applications, pp. 133–144 (2008)Google Scholar
  24. 24.
    Semantic Web. The Semantic Web (2010),
  25. 25.
    Slezak, D., Wroblewski, J., Eastwood, V., Synak, P.: Brighthouse: an analytic data warehouse for ad-hoc queries. In: Proceedings of the VLDB Endowment, vol. 1(2), pp. 1337–1345. ACM Digital Library (2008)Google Scholar
  26. 26.
    SPARQL Query Language for RDF,
  27. 27.
    Stumme, G., Taouil, R., Bastide, Y., Lakhal, L.: Conceptual Clustering with Iceberg Concept Lattices. In: Proceedings of GI-Fachgruppentreffen Maschinelles Lernen 2001, Universitat Dortmund (2001)Google Scholar
  28. 28.
    World Wide Web Consortium. Design Issues (2010),
  29. 29.
    White, P.W., French, C.D.: Database system with methodology for storing a database table by vertically partitioning all columns of the table. US Patent 5,794,229, August 11 (1998)Google Scholar
  30. 30.
    Wille, R.: Formal Concept Analysis as Mathematical Theory of concepts. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis: Foundations and Applications, pp. 1–6. Springer, Berlin (2005)CrossRefGoogle Scholar
  31. 31.
    Wolff, K.E.: A First Course in Formal Concept Analysis (1993),
  32. 32.
    Yevtushenko, S.: ConExp. (2006),
  33. 33.
    Zaki, M.J., Hsiao, C.-J.: Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure. IEEE Transactions on Knowledge and Data Mining 17(4) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Simon Andrews
    • 1
  • Constantinos Orphanides
    • 1
  • Simon Polovina
    • 1
  1. 1.Conceptual Structures Research Group, Communication and Computing Research Centre Faculty of Arts, Computing, Engineering and SciencesSheffield Hallam UniversitySheffieldUK

Personalised recommendations