Databases Reference
In-Depth Information
Fig. 2.6
Example of C
OG
Z fisheye distortion effect
the structural similarity between the ontologies using graph-based similarity tech-
niques. This information is combined with label similarity measures (e.g., Euclidean
distance, Hamming distance, substring distance) to produce a list of matching
correspondences.
Muse is a matching design wizard that uses data examples to help guide a user
through the matching design process [
Alexe et al. 2008
]. Like AlViz, Muse is still
in the early research phase and is not available for public download. The Muse tool
takes a different approach to user support by attempting to compile a small set of
yes/no questions that a designer can answer. The answers allow Muse to infer the
desired semantics of a potential matching correspondence. Muse also constructs
examples based on ontology instance data to help a user disambiguate a potential
correspondence with multiple interpretations.
The NeOn toolkit [
Le Duc et al. 2008
], developed as an Eclipse plugin,
3
is an
environment for managing ontologies within the NeOn project.
4
NeOn supports run
time and design time ontology matching support and can be extended via plugins.
The toolkit includes a matching editor called OntoMap, which allows a user to create
and edit matchings (see Fig.
2.9
). Similar to the previously mentioned tools, NeOn
supports OWL ontologies; however it also supports RDF and F-Logic. The toolkit
can convert a variety of sources (e.g., databases, file systems, UML diagrams) into
an ontology to be used for matching.
3
http://www.eclipse.org
.
4
http://www.neon-project.org
.
