Artificial Intelligence Algorithms for Natural Language Processing and
  the Semantic Web Ontology Learning
  Evolutionary clustering algorithms have considered as the most popular and
widely used evolutionary algorithms for minimising optimisation and practical
problems in nearly all fields. In this thesis, a new evolutionary clustering
algorithm star (ECA*) is proposed. Additionally, a number of experiments were
conducted to evaluate ECA* against five state-of-the-art approaches. For this,
32 heterogeneous and multi-featured datasets were used to examine their
performance using internal and external clustering measures, and to measure the
sensitivity of their performance towards dataset features in the form of
operational framework. The results indicate that ECA* overcomes its competitive
techniques in terms of the ability to find the right clusters. Based on its
superior performance, exploiting and adapting ECA* on the ontology learning had
a vital possibility. In the process of deriving concept hierarchies from
corpora, generating formal context may lead to a time-consuming process.
Therefore, formal context size reduction results in removing uninterested and
erroneous pairs, taking less time to extract the concept lattice and concept
hierarchies accordingly. In this premise, this work aims to propose a framework
to reduce the ambiguity of the formal context of the existing framework using
an adaptive version of ECA*. In turn, an experiment was conducted by applying
385 sample corpora from Wikipedia on the two frameworks to examine the
reduction of formal context size, which leads to yield concept lattice and
concept hierarchy. The resulting lattice of formal context was evaluated to the
original one using concept lattice-invariants. Accordingly, the homomorphic
between the two lattices preserves the quality of resulting concept hierarchies
by 89% in contrast to the basic ones, and the reduced concept lattice inherits
the structural relation of the original one.
