A Declarative Similarity Framework for Knowledge Intensive CBR by 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

The Similarity Framework

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

The Similarity Framework

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 2 Color: Dark Gray Price:  Case 1 Color: Dark Green Price:

Discussion Any questions?