7 research outputs found
Structuring Abstraction to Achieve Ontology Modularisation
Large and complex ontologies lead to usage difficulties, thereby hampering the ontology developers’
tasks. Ontology modules have been proposed as a possible solution, which is supported by some
algorithms and tools. However, the majority of types of modules, including those based on abstraction,
still rely on manual methods for modularisation. Toward filling this gap in modularisation techniques, we
systematised abstractions and selected five types of abstractions relevant for modularisation for which we
created novel algorithms, implemented them, and wrapped it in a GUI, called NOMSA, to facilitate their
use by ontology developers. The algorithms were evaluated quantitatively by assessing the quality of the
generated modules. The quality of a module is measured by comparing it to the benchmark metrics from
an existing framework for ontology modularisation. The results show that module’s quality ranges
between average to good, whilst also eliminating manual intervention
Pattern-based design applied to cultural heritage knowledge graphs
Ontology Design Patterns (ODPs) have become an established and recognised
practice for guaranteeing good quality ontology engineering. There are several
ODP repositories where ODPs are shared as well as ontology design methodologies
recommending their reuse. Performing rigorous testing is recommended as well
for supporting ontology maintenance and validating the resulting resource
against its motivating requirements. Nevertheless, it is less than
straightforward to find guidelines on how to apply such methodologies for
developing domain-specific knowledge graphs. ArCo is the knowledge graph of
Italian Cultural Heritage and has been developed by using eXtreme Design (XD),
an ODP- and test-driven methodology. During its development, XD has been
adapted to the need of the CH domain e.g. gathering requirements from an open,
diverse community of consumers, a new ODP has been defined and many have been
specialised to address specific CH requirements. This paper presents ArCo and
describes how to apply XD to the development and validation of a CH knowledge
graph, also detailing the (intellectual) process implemented for matching the
encountered modelling problems to ODPs. Relevant contributions also include a
novel web tool for supporting unit-testing of knowledge graphs, a rigorous
evaluation of ArCo, and a discussion of methodological lessons learned during
ArCo development