@misc{oai:ir.soken.ac.jp:00003142,
 author = {MENENDEZ MORA, Raul Ernesto and メヌンデス　モラ, ラウル　エルネスト and MENENDEZ MORA, Raul Ernesto},
 month = {2016-02-17, 2016-02-17},
 note = {  The semantic web provides a common framework that allows data to be shared and reused
across application, enterprise, and community boundaries. In order to achieve the goals
of the semantic web, it have to be able to define and to describe the relations among data
(i.e., resources) on the Web. Ontologies are one of the formal representation for organizing
information in the semantic web and they are also used in artificial intelligence,
systems engineering, software engineering, biomedical informatics, library science, enterprise
bookmarking, and information architecture as a form of knowledge representation
about the world or some part of it. In the semantic web context, since many actors provide
their own ontologies, ontology matching or ontology alignment has taken a critical
role for helping heterogeneous resources to inter-operate [23].
  Ontology matching tools find classes of data that are "semantically equivalent". This
process determine correspondences between concepts which are called alignments [22].
Finding those correspondences imply a semantic similarity assessment between the involved
concepts.
  Semantic similarity of words pairs is often represented by the similarity between the
concepts associated with the words. Several methods have been developed to compute
words similarity, most of them operating on taxonomic dictionaries like WordNet [24]
or external corpus like the Brown Corpus. However the majority of them suffer from a
serious limitation. They only focus on the semantic information shared by those words, or
in the semantic differences, but they have been rarely combined in a broader perspective.
  In this thesis we developed and applied a model of semantic similarity computation for
word pair comparison. This model consider the semantic commonalities and the semantic
differences as the core of its approach. By applying the model five new WordNet-based
semantic similarity measures for word pair comparison were created. Four of this semantic
similarity measures obtained higher values of correlation with human judgment than their
original expressions, while the fifth one remained as competitive as their original version.
  We also studyWordNet taxonomic properties to extend a corpus-independent information
content metric. The application of this new metric in one of the previously developed
node-based semantic similarity allowed us to obtain the highest value of correlation with
respect to human judgment. This thesis provides a general an extensible approach of
semantic similarity computation for word pair comparison., application/pdf, 総研大甲第1515号},
 title = {Improving Semantic Similarity Measures for Word Pair Comparison},
 year = {}
}