Abstract
Building a case base for a case-based reasoning (CBR) system is incomplete without similarity measures. For the attribute-value case structure similarity between values of an attribute should logically fit their relationship. Bipartite graphs have been shown to be a good representation of relationships between values of symbolic attributes and the diagnosis of the cases in a technical diagnosis CBR system, while using an asymmetrical weighted one-mode projection on the values to model their similarity.
However, the weighted one-mode projection assumes that the set of symbols is static, which is contradictory to the dynamic nature of case bases as defined by the retain phase of the CBR cycle. In this work we present two methods to update the similarity measure whenever new information is available and compare them. We show that even though updating the similarity measure to exactly reflect the case base had the new information been available a-priori produces better results, an imperfect update is a feasible, less time consuming temporary solution.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Commun. 7(1), 39–59 (1994)
Bach, K., Althoff, K.-D., Newo, R., Stahl, A.: A case-based reasoning approach for providing machine diagnosis from service reports. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS (LNAI), vol. 6880, pp. 363–377. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23291-6_27
Bach, K., Sauer, C., Althoff, K.D., Roth-Berghofer, T.: Knowledge modelling with the open source tool myCBR. In: CEUR Workshop Proceedings (2014)
Ceausu, V., Desprès, S.: A semantic case-based reasoning framework for text categorization. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 736–749. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_53
Cunningham, C., Weber, R., Proctor, J.M., Fowler, C., Murphy, M.: Investigating graphs in textual case-based reasoning. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 573–586. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28631-8_42
Cunningham, P.: A taxonomy of similarity mechanisms for case-based reasoning. IEEE Trans. Knowl. Data Eng. 21(11), 1532–1543 (2009)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Gómez-Martin, P.P., Gómez-Martin, M.A.: Case-based recommendation for online judges using learning itineraries. In: Case-Based Reasoning Research and Development: 25th International Conference, ICCBR (2017)
Markovitch, S., Scott, P.D.: The role of forgetting in learning. In: Machine Learning Proceedings (1988)
Reuss, P., Stram, R., Juckenack, C., Althoff, K.-D., Henkel, W., Fischer, D., Henning, F.: FEATURE-TAK - framework for extraction, analysis, and transformation of unstructured textual aircraft knowledge. In: Goel, A., DÃaz-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
Ricci, F., Avesani, P.: Learning a local similarity metric for case-based reasoning. In: International Conference on Case-Based Reasoning, pp. 301–312 (1995)
Sizov, G., Öztürk, P., Aamodt, A.: Evidence-driven retrieval in textual CBR: bridging the gap between retrieval and reuse. In: Hüllermeier, E., Minor, M. (eds.) ICCBR 2015. LNCS (LNAI), vol. 9343, pp. 351–365. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24586-7_24
Sizov, G., Öztürk, P., Štyrák, J.: Acquisition and reuse of reasoning knowledge from textual cases for automated analysis. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS (LNAI), vol. 8765, pp. 465–479. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11209-1_33
Stahl, A., Gabel, T.: Using evolution programs to learn local similarity measures. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 537–551. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45006-8_41
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
Zhou, T., Ren, J., Medo, M., Zhang, T.C.: Bipartite network projection and personal recommendation. Phys. Rev. E 76(4), 046115 (2007)
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Stram, R., Reuss, P., Althoff, KD. (2018). Dynamic Case Bases and the Asymmetrical Weighted One-Mode Projection. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_26
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