An ontology is a knowledge-representation structure, where words, terms or
concepts are defined by their mutual hierarchical relations. Ontologies are
becoming ever more prevalent in the world of natural language processing,
where we currently see a tendency towards using semantics for solving a
variety of tasks, particularly tasks related to information access.
Ontologies, taxonomies and thesauri (all related notions) are also used in
various variants by humans, to standardize business transactions or for
finding conceptual relations between terms in, e.g., the medical domain.
The acquisition of machine-readable, domain-specific semantic knowledge is
time consuming and prone to inconsistencies. The field of ontology learning
therefore provides tools for automating the construction of domain
ontologies (ontologies describing the entities and relations within a
particular field of interest), by analyzing large quantities of
domain-specific texts.
This thesis studies three main topics within the field of ontology
learning. First, we examine which sources of information are useful within
an ontology learning system and how the information sources can be combined
effectively. Secondly, we do this with a special focus on cross-language
text collections, to see if we can learn more from studying several
languages at once, than we can from a single-language text collection.
Finally, we investigate new approaches to formal and automatic evaluation
of the quality of a learned ontology.
We demonstrate how to combine information sources from different languages
and use them to train automatic classifiers to recognize lexico-semantic
relations. The cross-language data is shown to have a positive effect on
the quality of the learned ontologies. We also give theoretical and
experimental results, showing that our ontology evaluation method is a good
complement to and in some aspects improves on the evaluation measures in
use today.
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