Journal article
Refinement operators for directed labeled graphs with applications to instance-based learning
Knowledge-based systems, v 161, pp 425-441
01 Dec 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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, 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 in the context of instance-based machine learning. (C) 2018 Elsevier B.V. All rights reserved.
Metrics
3 Record Views
Details
- Title
- Refinement operators for directed labeled graphs with applications to instance-based learning
- Creators
- Santiago Ontanon - Drexel UniversityAli Shokoufandeh - Drexel University
- Publication Details
- Knowledge-based systems, v 161, pp 425-441
- Publisher
- Elsevier
- Number of pages
- 17
- Grant note
- IIS-1551338 / National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000452575500030
- Scopus ID
- 2-s2.0-85053324162
- Other Identifier
- 991019167731804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence