Information tuning with KARAT: Capitalizing on existing documents

Long Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1319)


Organizations store their information in electronic or paper documents. This information is severely underutilized in the daily work of most organizations. Because there are no effective means to access the documents, employees do not find relevant information, or are not even aware of its existence. We describe Information Tuning - a first step towards knowledge management in enterprises. Information Tuning capitalizes on existing documents and enables better exploitation of the contained knowledge by adding the background, context, and meta information which is necessary for making possible beneficial utilization, sharing, and reuse. We present an Information Tuning method which evolved from model-based knowledge acquisition from texts, and illustrate the method with application examples. Information Tuning is supported by the KARAT tool which applies techniques from text analysis and hypertext technology.


Knowledge Management Text Document Requirement Engineer Source Text Preparation Phase 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    A. Abecker, A. Bernardi, K. Hinkelmann, O. Kuhn, and M. Sintek. Towards a Well-Founded Technology for Organizational Memories. In: [12], 1997.Google Scholar
  2. [2]
    A. Abecker, St. Decker, K. Hinkelmann, and U. Reimer. Knowledge-Based Systems for Knowledge Management in Enterprises. Workshop at the 21st Annual German Conf. on AI (KI-97), Freiburg, Germany, September 1997.Google Scholar
  3. [3]
    A. Bernardi, M. Sintek, and A. Abecker. Combining Artificial Intelligence, Database Technology, and Hypermedia for Intelligent Fault Recording. Submitted. April 1997.Google Scholar
  4. [4]
    G. Bruno. Model-Based Software Engineering. Chapman & Hall, 1995.Google Scholar
  5. [5]
    Choo Chun Wei. Information Management for the Intelligent Organization: Roles and Implications for the Information Professions. 1995.Google Scholar
  6. [6]
    D. Clay, S. Geffner, J. Gottsegen, B. Gritton, and T. Smith. A General Framework for Constructing Conceptual Models of Metadata in Digital Libraries. In: First IEEE Metadata Conference, Silver Spring, Maryland, USA. April 1996.Google Scholar
  7. [7]
    A. M. Davis. Software Requirements, Objects, Functions and States. Englewood Cliffs, NJ: Prentice Hall, 1993.Google Scholar
  8. [8]
    H. S. Delugach. Analyzing Multiple Views of Software Requirements. In Nagle, Gerholz, and Eklund (Eds.), Conceptual Structures — Current Research and Practice, Ellis Horwood Limited, Chichester, England, 1992.Google Scholar
  9. [9]
    A. Dengel, R. Bleisinger, R Fein, R. Hoch, F. Hönes, and M. Malburg. Office-MAID — A System for Office Mail Analysis, Interpretation and Delivery. Proc. of First International Workshop on Document Analysis Systems (DAS'94), pages 253–275, Kaiserslautern, Germany, October 18–20 1994.Google Scholar
  10. [10]
    M. Dorfman and R. Thayer (Eds.). 11 Standards, Guidelines, and Examples of System and Software Requirements Engineering. Washington, D.C.: IEEE Computer Science Press, 1990.Google Scholar
  11. [11]
    A. P. Gabb and D. E. Henderson. Navy Specification Study Report 3: Requirements and Specification (DSTO-TR-0192). Salisbury, South Australia: DSTO Electronics and Surveillance Research Laboratory, 1995.Google Scholar
  12. [12]
    B. Gaines, M. A. Musen, et al. (eds.). AAAI Spring Symposium Artificial Intelligence in Knowledge Management. Stanford University, March, 1997.Google Scholar
  13. [13]
    R.V Guha. Towards a theory of meta-content. Scholar
  14. [14]
    P. J. Hayes, P M. Andersen, I. B. Nirenburg, and L. M. Schmandt. TCS: A Shell for Content-Based Text Categorization, Proc. of 6th Conference on A1 Applications, pages 320–326, Santa Barbara, CA, 1990.Google Scholar
  15. [15]
    R. Hoch. Using IR Techniques for Text Classification in Document Analysis. Proc. of 17th International Conference on Research and Development in Information Retrieval (SIGIR'94), pages 31–40, Dublin City, Ireland, July 3–6 1994.Google Scholar
  16. [16]
    J. Hofer-Alfeis and S. Klabunde. Approaches to Managing the Lessons Learned Cycle. In: [47]. 1996.Google Scholar
  17. [17]
    IEEE Standards Collection: Software Engineering. IEEE, 1993.Google Scholar
  18. [18]
    P S. Jacobs. Text-Based Intelligent Systems: Current Research and Practice in Information Retrieval. Lawrence Erlbaum, Hillsdale, 1992.Google Scholar
  19. [19]
    Knowledge Systems Laboratory, Institute for Information Technology, National Research Council Canada. FuzzyCLIPS Version 6.02A User's Guide. 1994.Google Scholar
  20. [20]
    O. Kühn and A. Abecker. Corporate Memories for Knowledge Management in Industrial Practice: Prospects and Challenges. Journal of Universal Computer Science. Springer Verlag, 1997. To appear.Google Scholar
  21. [21]
    O. Kühn and B. Höfling. Conserving Corporate Knowledge for Crankshaft Design. In: Seventh International Conference on Industrial & Engineering Applications of Artifical Intelligence & Expert Systems (IEA/AIE'94), Gordon and Breach Science Publishers. Also as DFKI RR-94-08. 1994Google Scholar
  22. [22]
    J. Liang and J. D. Palmer. A Pattern Matching and Clustering Based Approach for Supporting Requirements Transformation. Proc. of the First International Conference on Requirements Engineering (ICRE `94), April 1994.Google Scholar
  23. [23]
    D. Lukose. Knowledge Management Using MODEL-ECS. In: [12],1997.Google Scholar
  24. [24]
    O. Lutzy. Morphic-Plus: Ein morphologisches Analyseprogramm für die deutsche Flexionsmorphologie und Komposita-Analyse. DFKI Document D-95-07 (in German).Google Scholar
  25. [25]
    Marble Associates Inc. Leveraging Knowledge through a Corporate Memory Infrastructure. April 1994.Google Scholar
  26. [26]
    J. A. McDermid. Software Engineer's Reference Book. Oxford: Butterworth Heinemann Ltd., 1991.Google Scholar
  27. [27]
    J. A. McDermid, A. Vickers, and B. Whittle. Requirements Elicitation and Analysis: Goals, Problems and Approaches. Workshop on Requirements Elicitation for Software-based Systems (RESS), Keele, England, July 12–14 1994.Google Scholar
  28. [28]
    J.-U. Möller. Knowledge Acquisition from texts. Proc. of the European Knowledge Acquisition Workshop (EKAW'88), Gesellschaft für Mathematik and Datenverarbeitung mbH, Sankt Augustin, Germany, 1988.Google Scholar
  29. [29]
    K. Romhardt. Processes of Knowledge Preservation: Away from a Technology Dominated Approach. In: [2]. 1997. To appear.Google Scholar
  30. [30]
    G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. New York: McGraw Hill, 1983.Google Scholar
  31. [31]
    A.-W. Scheer. Architektur integrierter Informationssysteme, Grundlagen der Unternehmensmodellierung. 2nd edition, Springer Verlag, 1992.Google Scholar
  32. [32]
    F. Schmalhofer and B. Tschaitschian. Cooperative Knowledge Evolution for Complex Domains. In: G. Tecuci and Y. Kodratoff (eds). Machine Learning and Knowledge Acquisition — Integrated Approaches. Academic Press, 1995.Google Scholar
  33. [33]
    G. Schmidt. Modellbasierte, interaktive Wissensakquisition and Dokumentation von Domänenwissen (MIKADO), DISKI Vol. 90, infix Verlag, 1995.Google Scholar
  34. [34]
    M. L. G. Shaw and B. Gaines. Knowledge and Requirements Engineering. Proc. of the 10th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, Canada, 1995.Google Scholar
  35. [35]
    S. B. Shum. Representing Hard-to-Formalise, Contextualised, Multidisciplinary, Organisational Knowledge. In: [12], 1997.Google Scholar
  36. [36]
    S. B. Shum. Balancing Formality with Informality: User-Centred Requirements for Knowledge Management Technologies. In: [12], 1997.Google Scholar
  37. [37]
    D. Skuce. Hybrid KM: Integrating Documents, Knowledge Bases, and the Web. In: [12], 1997.Google Scholar
  38. [38]
    I. Sommerville. Software Engineering. Workingham, England: Addison Wesley, 1992.Google Scholar
  39. [39]
    E. W. Stein and V. Zwass. Actualizing Organizational Memory With Information Technology. Information Systems Research Vol. 6, No. 2: 85–117, 1995.Google Scholar
  40. [40]
    B. Tschaitschian, I. John, C. Wenzel. Integrating Knowledge Acquisition and Text Analysis for Requirements Engineering. Internal Report. DFKI, 1996.Google Scholar
  41. [41]
    B. Tschaitschian, C. Wenzel, and I. John. Tuning the quality of informal software requirements with KARAT.. In: E. Dubois, L. Opdahl, and K. Pohl (eds.). REFSQ'97. Third Int. Workshop on Requirements Engineering: Foundation for Software Quality. Held at CAiSE*97, Barcelona, 1997.Google Scholar
  42. [42]
    G. van Heijst, R. van der Spek, and E. Kruizinga. Organizing Corporate Memories. Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop KAW'96. November 1996.Google Scholar
  43. [43]
    C. Wenzel and R. Hoch. Text Categorization of Scanned Documents Applying a Rule-based Approach. Proc. of the Fourth Annual Symposium on Document Analysis and Information Retrieval (SDAIR'95), pages 333–346, 1995.Google Scholar
  44. [44]
    St. Wess: Intelligent Systems for Customer Support: Case-Based Reasoning in Help-Desk and Call-Center Applications. In: [2]. 1997. To appear.Google Scholar
  45. [45]
    B. J. Wielinga, A. T. Schreiber, and J. A. Breuker. KADS: A Modelling Approach to Knowledge Engineering. Knowledge Acquisition, 4(1), 1992.Google Scholar
  46. [46]
    D. P Wood, M. G. Christel, and S. M. Stevens. A Multimedia Approach to Requirements Capture and Modeling. Proc. of the First International Conference on Requirements Engineering (ICRE `94), pages 53–56, Colorado Springs, CO, April 18–22 1994.Google Scholar
  47. [47]
    M. Wolf and U. Reimer (eds). PAKM-96: First Int. Conference on Practical Aspects of Knowledge Management. Basel, Switzerland, October 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  1. 1.German Research Center for Artificial Intelligence (DFKI) GmbHKaiserslauternGermany

Personalised recommendations