Conference proceeding
On Similarity Measures Based on a Refinement Lattice
CASE-BASED REASONING RESEARCH AND DEVELOPMENT, PROCEEDINGS, v 5650, pp 240-255
01 Jan 2009
Abstract
Retrieval of structured cases using similarity has been studied in CBR, but there has been less activity oil defining similarity on description logics (DL). In this paper we present an approach that allows us to present two similarity measures for feature logics, a subfamily of DLs, based oil the concept of refinement lattice. The first one is based on computing the anti-unification (AU) of two cases to assess the amount of shared information. The second measure decomposes the cases into a set of independent properties, and then assesses flow many of these properties are shared between the two cases. Moreover, we show that the defined measures are applicable to any representation language for which a refinement lattice can be defined. We empirically evaluate our measures comparing them to other measures in the literature in a variety of relational data sets showing very good results.
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Details
- Title
- On Similarity Measures Based on a Refinement Lattice
- Creators
- Santiago Ontanon - Georgia Institute of TechnologyEnric Plaza - Artificial Intelligence Research Institute
- Contributors
- L McGinty (Editor)D C Wilson (Editor)
- Publication Details
- CASE-BASED REASONING RESEARCH AND DEVELOPMENT, PROCEEDINGS, v 5650, pp 240-255
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 3
- Grant note
- TIN200615140C03-01. / MID-CBR
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000271335600018
- Scopus ID
- 2-s2.0-70350373676
- Other Identifier
- 991021869109804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods