Automating modularisation with algorithms for abstraction and expressiveness

Tracking #: 1614-2826

This paper is currently under review
Authors: 
Zubeida Khan
Maria Keet

Responsible editor: 
Bernardo Cuenca Grau

Submission type: 
Full Paper
Abstract: 
Large and complex ontologies lead to usage difficulty for both humans and software tools, hampering the ontology developers’ tasks. Modularity has been proposed as a possible solution to this problem and a number of techniques and tools for ontology modularisation have been developed in recent years. These algorithms and tools allow the developer to create only a subset of the types of modules they wish to create, employing principally partitioning and locality-based techniques. Different types of abstraction and expressiveness modules, on the other hand, still heavily rely on manual methods for modularisation. We propose here to fill this gap in modularisation techniques. We present five new algorithms to generate abstraction and expressiveness modules. They have been implemented in the NOMSA tool for modularising ontologies and were evaluated by both comparing it to other modularisation tools using a set of existing modules and assessing the quality of the generated modules. The results show that the algorithms’ performance is as good as others, whilst also eliminating manual intervention. The module’s quality ranges between average to good. Further, the algorithms are wrapped in an easily usable GUI to facilitate their use by ontology developers.
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