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Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework

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Book cover Case-Based Reasoning Research and Development (ICCBR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11680))

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Abstract

This paper describes the learning of new similarity values for existing measures within the framework FEATURE-TAK. Maintenance of similarity measures is not easy, especially when having a semi-automated approach to relieve the knowledge engineer. Based on the extension of the vocabulary, the newly added values have to be integrated into the similarity measures with an initial similarity value to be useful. We describe the extension of the similarity measures with automated taxonomy extension and one-mode projections and present a comprehensive evaluation and comparison between the different approaches to highlight the advantages and short comings.

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References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Commun. 7(1), 39–59 (1994). http://www.idi.ntnu.no/emner/it3794/lectures/papers/Aamodt_1994_Case.pdf

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)

    Google Scholar 

  3. Bach, K.: Knowledge acquisition for case-based reasoning systems. Ph.D. thesis, University of Hildesheim (2013). Dr. Hut Verlag München

    Google Scholar 

  4. Bach, K., Althoff, K.-D.: Developing case-based reasoning applications using myCBR 3. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS (LNAI), vol. 7466, pp. 17–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32986-9_4

    Chapter  Google Scholar 

  5. Borgelt, C.: An implementation of the FP-growth algorithm. In: Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp. 1–5. ACM (2005)

    Google Scholar 

  6. Hundt, A., Reuss, P., Sauer, C.S., Roth-Berhofer, T.: Knowledge modeling and maintenance in myCBR3. In: Proceedings of the FGWM Workshop at Learning, Knowledge, Adaptation Conference 2014. Springer, Heidelberg (2014)

    Google Scholar 

  7. Lambiotte, R., Ausloos, M.: N-body decomposition of bipartite author networks. Phys. Rev. E 72, 066117 (2005)

    Article  Google Scholar 

  8. de Mantaras, R.L.: Case-based reasoning. In: Advanced Course on Artificial Intelligence, pp. 127–145. Springer, Heidelberg (1999)

    Google Scholar 

  9. Reuss, P., Stram, R., Althoff, K.-D., Henkel, W., Henning, F.: Knowledge engineering for decision support on diagnosis and maintenance in the aircraft domain. In: Nalepa, G.J., Baumeister, J. (eds.) Synergies Between Knowledge Engineering and Software Engineering. AISC, vol. 626, pp. 173–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64161-4_9

    Chapter  Google Scholar 

  10. Reuss, P., et al.: FEATURE-TAK - framework for extraction, analysis, and transformation of unstructured textual aircraft knowledge. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 327–341. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_22

    Chapter  Google Scholar 

  11. Stahl, A., Roth-Berghofer, T.R.: Rapid prototyping of CBR applications with the open source tool myCBR. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 615–629. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85502-6_42

    Chapter  Google Scholar 

  12. Stram, R., Reuss, P., Althoff, K.-D.: Weighted one mode projection of a bipartite graph as a local similarity measure. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 375–389. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_26

    Chapter  Google Scholar 

  13. Stram, R., Reuss, P., Althoff, K.-D.: Dynamic case bases and the asymmetrical weighted one-mode projection. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 385–398. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_26

    Chapter  Google Scholar 

  14. Stram, R., Reuss, P., Althoff, K.-D., Henkel, W., Fischer, D.: Relevance matrix generation using sensitivity analysis in a case-based reasoning environment. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 402–412. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_27

    Chapter  Google Scholar 

  15. Xu, K., Wang, F., Gu, L.: Behaviour analysis of internet traffic via bipartite graphs and one-mode projecions. IEEE/ACM Trans. Netw. (TON) 22, 931–942 (2014)

    Article  Google Scholar 

  16. Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Phys. Rev. E 76, 046115 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. German Research Center for Artificial Intelligence, Kaiserslautern, Germany

    Pascal Reuss, Rotem Stram & Klaus-Dieter Althoff

  2. Institute of Computer Science, Intelligent Information Systems Lab, University of Hildesheim, Hildesheim, Germany

    Pascal Reuss & Klaus-Dieter Althoff

  3. Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany

    Oliver Berg & Rotem Stram

Authors
  1. Oliver Berg
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  2. Pascal Reuss
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  3. Rotem Stram
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  4. Klaus-Dieter Althoff
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Corresponding author

Correspondence to Pascal Reuss .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Kerstin Bach

  2. Ohio University, Athens, OH, USA

    Cindy Marling

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Berg, O., Reuss, P., Stram, R., Althoff, KD. (2019). Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework. In: Bach, K., Marling, C. (eds) Case-Based Reasoning Research and Development. ICCBR 2019. Lecture Notes in Computer Science(), vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-29249-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29248-5

  • Online ISBN: 978-3-030-29249-2

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