Refinement operators for directed labeled graphs with applications to instance-based learning

This paper presents a collection of refinement operators for directed labeled graphs (DLGs), and a family of distance and similarity measures based on them. We build upon previous work on refinement operators for other representations such as feature terms and description logic models. Specifically,...

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Bibliographic Details
Published inKnowledge-based systems Vol. 161; pp. 425 - 441
Main Authors Ontañón, Santiago, Shokoufandeh, Ali
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.12.2018
Elsevier Science Ltd
Subjects
Artificial intelligence
Case-based reasoning
Directed labeled graphs
Distance measures
Expert systems
Graphs
Instance-based learning
Intelligent systems
Machine learning
Operators
Refinement operators
Similarity
Similarity assessment
Directed labeled graphs
Instance-based learning
Similarity assessment
Refinement operators
Distance measures
Case-based reasoning
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Summary:This paper presents a collection of refinement operators for directed labeled graphs (DLGs), and a family of distance and similarity measures based on them. We build upon previous work on refinement operators for other representations such as feature terms and description logic models. Specifically, we present eight refinement operators for DLGs, which will allow for the adaptation of three similarity measures to DLGs: the anti-unification-based, Sλ, the property-based, Sπ, and the weighted property-based, Swπ, similarities. We evaluate the resulting measures empirically, comparing them to existing similarity measures for structured data in the context of instance-based machine learning.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.08.006