1. ICCBR’07 Workshop Program David C. Wilson Deepak Khemani

2. Workshops CBR and Context-Awareness –Lorcan Coyle, Sven Schwarz Knowledge Discovery and Similarity –David Wilson, Deepak Khemani Uncertainty and Fuzzines in CBR –Eyke Hüllermeier, Michael Richter, Rosina Weber CBR in the Health Sciences –Isabelle Bichindaritz, Stefania Montani Textual CBR: Beyond Retrieval –Derek Bridge, Paulo Gomes, Nuno Seco

3. Knowledge Discovery and Similarity Workshop David C. Wilson Deepak Khemani

4. Program Committee Enrico Blanzieri, University of Trento, Italy Karl Branting, MITRE, USA Mirjam Minor, Universität Trier, Germany Rosina Weber, Drexel University, USA Qiang Yang, Hong Kong University of Science and Technology, Hong Kong

5. Overview Novel aspects of similarity –Framework –Learning value functions –User-specific similarity knowledge –Structural similarity for metal casing design Modeling and Interaction –Learning similarity via adaptation knowledge –Deriving similarity support for new language domain –Visualization to assess similarity

6. Non Standard Similarity Metrics Pádraig Cunningham, Sarah Jane Delany Taxonomy of Similarity Measures –Feature Space –Information Theoretic –Transformational –Emergent

7. Feature space –notion of problem space and decision surface –notion of noise – wrong labels and feature values –intrinsic similarity measures –dot product for bags of words in TCBR Information theoretic –includes compression based – if a document A is similar to a document B then the concatenated documents would compress more than they would individually –extrinsic similarity – no notion of feature based noise

8. Transformational –edit distance, sequence alignment –computationally expensive –similarity  how easy it is to adapt Emergent –random forests, cluster kernels –extrinsic measures, collaborative determination –for example, brands preferred by young adults –non problem space, more amenable to knowledge discovery

10. Similarity in Reinforcement Learning Peng Zang, Charles Lee Isabell Jr. Policy in reinforcement learning Value of a state – aggregated expected reward similarity – context dependent functional perspective – selection of cases –issues of relevance and applicability –two cases are similar if there is statistical dependence between them

12. Value function approximation –states = cases Based on probability of reaching a state –retrieves value of a state –reuse = backup of values

13. Similarity viewed as relevance and applicability Relevance formalized on statistical dependence In RL, this led to the successor relationship Future work – Make case retrieval scale – Retention policy

14. Towards Personalized Interfaces for Similarity Adjustment In Case Based Recommender Systems Todd Holloway, David Leake Recommender systems Users customize similarity knowledge Design patterns for creating CBR systems Prototype – movie recommendation Netflix, imdb databases

15. Personalized feature spaces –based on mining Netflix databases Users can tweak weights in similarity function Guidelines for representation –succinct vocabulary –(yet) sufficiently expressive –comprehensibility

16. Refining Similarity Measures for Effective Reuse of Metal Casting Design Knowledge Miltos Petridis, Soran Saeed, Brian Knight Metal Casting design –number and location of feeders –speed of pouring –location of chilling Knowledge reuse dependent on spatial structural features –obtained from experts –2D sections of shapes instead of 3D shapes –blueprints of drawings

17. The casting design problem 17 Typical design decisions: Number and positions of feeders, chills Orientation Feed speed The quest is to avoid shrinkage and porosity

18. Features –bends and corners –aspect ratio of shapes (long -> use chill) –shape orientation (location of features) Case representation –number of generic shapes connected together –graph of components

19. Similarity – combination of –count components of different types –cycles and leaves in the graph –maximum common subgraphs (MCS) MCS is intrinsically a hard problem (but) greedy algorithms work well Enhanced similarity –increased use of geometric information

20. Defined similarity measures between 3D shapes for retrieving useful advice on metal casting Following the reasoning process of the expert guides well the definition of similarity measures. Evaluation results are positive Enhanced similarity measures improve the quality of advice Shape CBR automates the process and integrates within current engineering design workflow 20

21. Interactive Knowledge Acquisition in Case Based Reasoning Amélie Cordier, Béatrice Fuchs, Jean Lieber, Alain Mille Principle –A system produces a failure –The system misses some knowledge to reason properly –Knowledge must evolve to avoid the failure to appear again Exploiting failures in CBR –Riesbeck and Schank : test, explain, repair –Aamodt and Plaza : revise –Hammond : CHEF –Cox : Meta-Aqua

22. Interactive –From experts, when other approaches fail –Opportunistic, to reduce the knowledge engineering effort Why ? –To add new knowledge to the initial knowledge base –System + K0 = untrained system –System + Kn = experienced system –...

23. Two prototypes for interactive knowledge acquisition from failures –FrakaS-p : solution inconsistent with the expert knowledge or incomplete solution –IakA-p : approximation error too large according to the expert IakA-p exemplifies the IakA approach FrakaS-p exemplifies the FrakaS approach

26. Domain Modeling in TCBR: How to Understand a New Application Domain Kerstin Bach, Alexandre Hanft Preliminaries: Modelling IEs used for Retrieval in CRN Goal: Model an unknown domain –extracting IEs from textsections from cases. No comprehensive vocabulary for German exist –Heterogeneous repositories can be used building a vocabulary repository to cope with unknown words in new application domains.

27. Integration of heterogenous repositories to build up a domain knowledge –GermaNet –Projekt Deutscher Wortschatz Evaluation Sample Application DoMHIR –Domain Modeling and Integration of Heterogeneous Repositories Textual Coverage Rate (TCR)

28. Sample Application DoMIHR (Domain Modeling and Integration of Hetero­geneous Repositories) a tool which supports a knowledge engineer to define a case format for a given database which can be used to create a TCBR system In contrast to other apps we facilitate the knowledge engineer to explicitly model misspelled words as terms of the right (spelled) IE. –Offers possibility to change the misspelled word into right form

29. Sample Application

30. Case-Based Reasoning Visualization and Case Quality Analysis Ying Zhang, Panos Louvieris, Maria Petrou

31. Visualization methods Principal Component Analysis (PCA)---- computationally light, linear. –Interactive nonlinear: CCA, principal curve, principal surface, computationally expensive Multidimensional scaling (MDS)---- nonlinear –Sammon Mapping, no mapping function, can not accommodate new data SOM---nonlinear, preserve topological structures Data are close by are usually projected to nearby nodes, show the relationship between data and clusters. Not faithfully portray the distribution of the data and its structure. It does not directly apply to scaling, does not show the distance between the neurons, need U-Matrix. Visualization induced SOM (ViSOM)

32. ViSOM Use similar grid structures as normal SOM. Instead of updating the neighbourhood by ViSOM updates the neighbourhood weights by The distance between the neurons on the map can reflect the corresponding distances in the original data space. According to the experiments, ViSOM result similar to Sammon Mapping, and more details of intra cluster and inter-pointer distribution is provided. Sammon Mapping is not suitable for CBR. SOM is very popular in visualization and clustering, many ready software and toolbox available, can be easily updated for ViSOM.

33. Problem distribution regularity Demonstrate whether the new problem is close to the previous cases or not. Insufficient cases or suitability of applying CBR Hot spot, reorganize the case base to speed up the case retrieval. Case base structure, discover the patterns and trends in case base.

34. Problem solution regularity 01101101 10010010

35. Using ViSOM For solution which is single dimensional and categorical, such as Iris data. For solution which is single dimensional and numerical, colour and grey tone can express the solution value directly. For solution which is multi-dimensional, colour and grey tone can be used to express the solution difference between the case and its neighbours. If the larger the solution difference is, the lighter the colour of the neurons is, thus, a case base with dark colour neurons is more suitable for CBR If necessary, two maps can be set up, on for case problems space and the other for solution space. Can help evaluate the difficult of adaptation. Visualization can provide the intuitive insight of the case base, a more specific measurement is needed.