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Declarative Similarity Framework for Knowledge Intensive CBR

This presentation discusses the CBROnto terminology and the use of description logics and LOOM in CBR systems. It also explores the similarity framework, including user-defined relevance criteria and different approaches to case retrieval.

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Declarative Similarity Framework for Knowledge Intensive CBR

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  1. A Declarative Similarity Framework for Knowledge Intensive CBRby Díaz-Agudo and González-Calero Presented by Ida Sofie G Stenerud 25.October 2006

  2. Terminology • CBROnto • Description Logic • LOOM

  3. CBROnto • ”A task based ontology compromising common CBR terminology” • A vocabulary for expressing the CBR elements • CBROnto’s 2 purposes: • To integrate CBR process knowledge and domain knowledge • To be a domain-indepentent framework for designing CBR systems

  4. Description Logics • A knowledge representation language • Formal objects: • Concepts • Relations • Individuals • Reasoning mechanisms • Subsumption • Instance Recognition

  5. LOOM • A description logic implementation • A query language for CBR • Example:

  6. The Similarity Framework • Several similarity measures can coexist at one time • Several approaches to case retrieval: • Relevance Criteria • User-defined or pre-defined in program • Similarity Criteria • Declarative represetation of differences and similarity • Representational approach • ”Semantic traversal” of the hierarchy • Computational approach • Can be a combination of all the above

  7. The Similarity Framework

  8. User-defined Relevance Criteria • Relevance: Why is this case more relevant than other cases? • More-on-point, Most-on-point

  9. Similarity Terms • Explicit computation of similarity terms • Declaratively • Similarity: ”the most specific concept which subsumes 2 cases”

  10. Representational Approach • Assignment of similarity meanings to the path between cases • The Generic Travel Operator

  11. Computational Approach • Different alternatives to compute numeric similarity between attributes • Nearest Neighbour Algorithm • The classic global similarity approach • For common attributes: use local measure • For common relations: use global measure to compare related sub-objects • Similarity is a weighted sum of these • NB: Only attribute level, not instance level similarity!

  12. Intra-class vs. inter-class similarity • Intra-class similarity: • Dependent on the attribute fillers • Inter-class similarity • Dependent on object position in hierarchy • Multiplied to get final similarity result

  13. The Similarity Framework

  14. Similarity Functions • Local similarity • Similarity between two values of a type • Global similarity • Defines how you combine local similarities • Positional similarity • Indepentent of attribute values, only dependent on position in hierarchy

  15. A Case Matching Example • Case 1 • Color: Dark Green • Price: 1 500 000 • Case 2 • Color: Dark Gray • Price: 1 100 000

  16. Discussion Any questions?

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