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

摘要

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.