Ontology search, which is the process of finding ontologies or
ontological terms for users’ defined queries from an ontology
collection, is an important task to facilitate ontology reuse of
ontology engineering. Ontology reuse is desired to avoid the
tedious process of building an ontology from scratch and to limit
the design of several competing ontologies that represent similar
knowledge. Since many organisations in both the private and
public sectors are publishing their data in RDF, they
increasingly require to find or design ontologies for data
annotation and/or integration. In general, there exist multiple
ontologies representing a domain, therefore, finding the best
matching ontologies or their terms is required to facilitate
manual or dynamic ontology selection for both ontology design and
data annotation.
The ranking is a crucial component in the ontology retrieval
process which aims at listing the ‘relevant0 ontologies or
their terms as high as possible in the search results to reduce
the human intervention. Most existing ontology ranking techniques
inherit one or more information retrieval ranking parameter(s).
They linearly combine the values of these parameters for each
ontology to compute the relevance score against a user query and
rank the results in descending order of the relevance score. A
significant aspect of achieving an effective ontology ranking
model is to develop novel metrics and dynamic techniques that can
optimise the relevance score of the most relevant ontology for a
user query.
In this thesis, we present extensive research in ontology
retrieval and ranking, where several research gaps in the
existing literature are identified and addressed. First, we begin
the thesis with a review of the literature and propose a taxonomy
of Semantic Web data (i.e., ontologies and linked data) retrieval
approaches. That allows us to identify potential research
directions in the field. In the remainder of the thesis, we
address several of the identified shortcomings in the ontology
retrieval domain. We develop a framework for the empirical and
comparative evaluation of different ontology ranking solutions,
which has not been studied in the literature so far. Second, we
propose an effective relationship-based concept retrieval
framework and a concept ranking model through the use of learning
to rank approach which addresses the limitation of the existing
linear ranking models. Third, we propose RecOn, a framework that
helps users in finding the best matching ontologies to a
multi-keyword query. There the relevance score of an ontology to
the query is computed by formulating and solving the ontology
recommendation problem as a linear and an optimisation problem.
Finally, the thesis also reports on an extensive comparative
evaluation of our proposed solutions with several other
state-of-the-art techniques using real-world ontologies. This
thesis will be useful for researchers and practitioners
interested in ontology search, for methods and performance
benchmark on ranking approaches to ontology search
