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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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A Declarative Similarity Framework for Knowledge Intensive CBRby Díaz-Agudo and González-Calero Presented by Ida Sofie G Stenerud 25.October 2006
Terminology • CBROnto • Description Logic • LOOM
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
Description Logics • A knowledge representation language • Formal objects: • Concepts • Relations • Individuals • Reasoning mechanisms • Subsumption • Instance Recognition
LOOM • A description logic implementation • A query language for CBR • Example:
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
User-defined Relevance Criteria • Relevance: Why is this case more relevant than other cases? • More-on-point, Most-on-point
Similarity Terms • Explicit computation of similarity terms • Declaratively • Similarity: ”the most specific concept which subsumes 2 cases”
Representational Approach • Assignment of similarity meanings to the path between cases • The Generic Travel Operator
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!
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
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
A Case Matching Example • Case 1 • Color: Dark Green • Price: 1 500 000 • Case 2 • Color: Dark Gray • Price: 1 100 000
Discussion Any questions?