Automatic Evaluation of Ontologies - Natural Language Processing and Text Mining - page 190

11
Automatic Evaluation of Ontologies
Janez Brank, Marko Grobelnik, and Dunja Mladenic
11.1 Introduction
We can observe that the focus of modern information systems is moving from “data
processing” towards “concept processing,” meaning that the basic unit of processing
is less and less an atomic piece of data and is becoming more a semantic concept
which carries an interpretation and exists in a context with other concepts. An
ontology is commonly used as a structure capturing knowledge about a certain area
by providing relevant concepts and relations between them. Analysis of textual data
plays an important role in construction and usage of ontologies, especially with the
growing popularity of semi-automated ontology construction (here referred to also
as ontology learning). Different knowledge discovery methods have been adopted for
the problem of semi-automated ontology construction [10] including unsupervised,
semi-supervised and supervised learning over a collection of text documents, using
natural language processing to obtain semantic graph of a document, visualization of
documents, information extraction to find relevant concepts, visualization of context
of named entities in a document collection.
A key factor which makes a particular discipline or approach scientific is the abil-
ity to evaluate and compare the ideas within the area. Ontologies are a fundamental
data structure for conceptualizing knowledge which is in most practical cases soft
and non-uniquely expressible. As a consequence, we are in general able to build many
different ontologies conceptualizing the same body of knowledge and we should be
able to say which of these ontologies serves better some predefined criterion. Thus,
ontology evaluation is an important issue that must be addressed if ontologies are
to be widely adopted in the semantic web and other semantics-aware applications.
Users facing a multitude of ontologies need to have a way of assessing them and
deciding which one best fits their requirements. Likewise, people constructing an
ontology need a way to evaluate the resulting ontology and possibly to guide the
construction process and any refinement steps. Automated or semi-automated on-
tology learning techniques also require effective evaluation measures, which can be
used to select the best ontology out of many candidates, to select values of tunable
parameters of the learning algorithm, or to direct the learning process itself if the
latter is formulated as finding a path through a search space.