Combining Bottom-Up and Top-Down Generation of Interactive Knowledge Maps for Enterprise Search

  • Michael Kaufmann
  • Gwendolin Wilke
  • Edy Portmann
  • Knut Hinkelmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8793)


Our research project develops an intranet search engine with concept-browsing functionality, where the user is able to navigate the conceptual level in an interactive, automatically generated knowledge map. This knowledge map visualizes tacit, implicit knowledge, extracted from the intranet, as a network of semantic concepts. Inductive and deductive methods are combined; a text analytics engine extracts knowledge structures from data inductively, and the enterprise ontology provides a backbone structure to the process deductively. In addition to performing conventional keyword search, the user can browse the semantic network of concepts and associations to find documents and data records. Also, the user can expand and edit the knowledge network directly. As a vision, we propose a knowledge-management system that provides concept-browsing, based on a knowledge warehouse layer on top of a heterogeneous knowledge base with various systems interfaces. Such a concept browser will empower knowledge workers to interact with knowledge structures.


knowledge technology knowledge engineering concept extraction enterprise ontology enterprise search concept browsing 


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  1. 1.
    Margolis, E., Laurence, S.: Concepts. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2014)Google Scholar
  2. 2.
    Guarino, N.: Formal Ontology, Conceptual Analysis and Knowledge Representation. Int. J. Hum-Comput. Stud. 43, 625–640 (1995)CrossRefGoogle Scholar
  3. 3.
    Österle, H., Becker, J., Frank, U., Hess, T., Karagiannis, D., Krcmar, H., Loos, P., Mertens, P., Oberweis, A., Sinz, E.J.: Memorandum on design-oriented information systems research. Eur. J. Inf. Syst. 20, 7–10 (2010)CrossRefGoogle Scholar
  4. 4.
    Preece, A., Flett, A., Sleeman, D., Curry, D., Meany, N., Perry, P.: Better knowledge management through knowledge engineering. IEEE Intell. Syst. 16, 36–43 (2001)CrossRefGoogle Scholar
  5. 5.
    Shadbolt, N.: Knowledge Technologies. Ingenia R. Acad. Eng. 58–61 (2001)Google Scholar
  6. 6.
    Milton, N., Shadbolt, N., Cottam, H., Hammersley, M.: Towards a knowledge technology for knowledge management. Int. J. Hum.-Comput. Stud. 51, 615–641 (1999)CrossRefGoogle Scholar
  7. 7.
    Yacci, M.: The Knowledge Warehouses Reusing Knowledge Components. Perform. Improv. Q. 12, 132–140 (1999)CrossRefGoogle Scholar
  8. 8.
    Frisch, A.M.: Knowledge Retrieval as Specialized Inference. Ph.D Thesis, University of Rochester (1986)Google Scholar
  9. 9.
    Nemati, H.R., Steiger, D.M., Iyer, L.S., Herschel, R.T.: Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decis. Support Syst. 33, 143–161 (2002)CrossRefGoogle Scholar
  10. 10.
    Nilsson, M., Palmér, M.: Conzilla - Towards a Concept Browser (No. CID-53, TRITA-NA-D9911). Stockholm: Centre for User Oriented IT Design, Dept. Computing Science, Royal Institute of Technology KTH (1999)Google Scholar
  11. 11.
    Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press (2001)Google Scholar
  12. 12.
    Eysenck, M.W., Keane, M.T.: Cognitive Psychology: A Student’s Handbook, 4th edn. Psychology Press (2000)Google Scholar
  13. 13.
    McClelland, J.L., Cleeremans, A.: Consciousness and Connectionist Models. In: McClelland, J.L., Bayne, T., and Wilken, P. (eds.) The Oxford Companion to Consciousness. Oxford University Press (2009)Google Scholar
  14. 14.
    Cudré-Mauroux, P., Liu, L., Özsu, M.T.: Emergent Semantics. Encyclopedia of Database Systems, pp. 982–985 Google Scholar
  15. 15.
    Portmann, E., Pedrycz, W.: Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management. In: Papageorgiou, E.I. (ed.) Fuzzy Cognitive Maps for Applied Sciences and Engineering. ISRL, vol. 54, pp. 89–105. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  16. 16.
    Maedche, A., Staab, S.: Ontology learning for the Semantic Web. IEEE Intell. Syst. 16, 72–79 (2001)CrossRefGoogle Scholar
  17. 17.
    Parameswaran, A., Garcia-Molina, H., Rajaraman, A.: Towards the Web of Concepts: Extracting Concepts from Large Datasets. Proc VLDB Endow. 3, 566–577 (2010)CrossRefGoogle Scholar
  18. 18.
    Deerwester, S.: Improving Information Retrieval with Latent Semantic Indexing. Presented at the Proceedings of the 51st ASIS Annual Meeting (ASIS 1988) (October 23, 1988)Google Scholar
  19. 19.
    Ganter, B., Bock, H.H.: Software for formal concept analysis. Classification as a tool of research, pp. 161–167. North-Holland, Amsterdam (1986)Google Scholar
  20. 20.
    Portmann, E., Kaufmann, M.A., Graf, C.: A Distributed, Semiotic-inductive, and Human-oriented Approach to Web-scale Knowledge Retrieval. In: Proceedings of the 2012 International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning, pp. 1–8. ACM, New York (2012)CrossRefGoogle Scholar
  21. 21.
    Hinkelmann, K., Merelli, E., Thönssen, B.: The Role of Content and Context in Enterprise Repositories. Presented at the 2nd International Workshop on Advanced Enterprise Architecture and Repositories (AER) (2010)Google Scholar
  22. 22.
    Thönssen, B.: An Enterprise Ontology Building the Bases for Automatic Metadata Generation. In: Sánchez-Alonso, S., Athanasiadis, I.N. (eds.) MTSR 2010. CCIS, vol. 108, pp. 195–210. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    The Open Group: ArchiMate 2.1 Specification,
  24. 24.
    Martin, A., Emmenegger, S., Wilke, G.: Integrating an enterprise architecture ontology in a case-based reasoning approach for project knowledge. In: Proceedings of the Enterprise Systems Conference, ES (2013)Google Scholar
  25. 25.
    Thönssen, B.: Automatic, Format-independent Generation of Metadata for Documents Based on Semantically Enriched Context Information. Ph.D Thesis, University of Camarino (2013)Google Scholar
  26. 26.
    Ichikawa, J.J., Steup, M.: The Analysis of Knowledge. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2014)Google Scholar
  27. 27.
    Hawthorne, J.: Inductive Logic. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (2012)Google Scholar
  28. 28.
    Shi, L., Griffiths, T.L.: Neural implementation of hierarchical bayesian inference by importance sampling. Advances in Neural Information Processing Systems 22, 1669–1677 (2009)Google Scholar
  29. 29.
    Kaufmann, M.: Inductive Fuzzy Classification in Marketing Analytics. Springer (2014)Google Scholar
  30. 30.
    The Apache Software Foundation: Apache Lucene - Apache Lucene Core,
  31. 31.
    Agrawal, R., Imieliński, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM, New York (1993)CrossRefGoogle Scholar
  32. 32.
    Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Hilbert, M., López, P.: The World’s Technological Capacity to Store, Communicate, and Compute Information. Science 332, 60–65 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michael Kaufmann
    • 1
  • Gwendolin Wilke
    • 2
  • Edy Portmann
    • 3
  • Knut Hinkelmann
    • 2
  1. 1.Lucerne University of Applied Sciences and ArtsHorwSwitzerland
  2. 2.University of Applied Sciences North-Western SwitzerlandOltenSwitzerland
  3. 3.University of BernSwitzerland

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