Conference proceeding
Refinement-Based Similarity Measures for Directed Labeled Graphs
CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2016, v 9969, pp 311-326
01 Jan 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper presents a collection of similarity measures based on refinement operators for directed labeled graphs (DLGs). We build upon previous work on refinement operators for other representation formalisms such as feature terms and description logics. Specifically, we present refinement operators for DLGs, which enable the adaptation of three similarity measures to DLGs: the anti-unification-based, S-lambda,the property-based, S-pi, and the weighted property-based, S-w pi, similarities. We evaluate the resulting measures empirically comparing them to existing similarity measures for structured data.
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Details
- Title
- Refinement-Based Similarity Measures for Directed Labeled Graphs
- Creators
- Santiago Ontanon - Drexel UniversityAli Shokoufandeh - Drexel University
- Contributors
- A Goel (Editor)M B DiazAgudo (Editor)T RothBerghofer (Editor)
- Publication Details
- CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2016, v 9969, pp 311-326
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 16
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000389799100021
- Scopus ID
- 2-s2.0-84994803434
- Other Identifier
- 991019167736404721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems