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  1. 020 学位論文
  2. 複合科学研究科
  3. 17 情報学専攻

Improving Semantic Similarity Measures for Word Pair Comparison

https://ir.soken.ac.jp/records/3142
https://ir.soken.ac.jp/records/3142
7c8d48b0-cc8f-4e19-8aa3-46690c1019b8
名前 / ファイル ライセンス アクション
甲1515_要旨.pdf 要旨・審査要旨 (282.1 kB)
甲1515_本文.pdf 本文 (2.7 MB)
Item type 学位論文 / Thesis or Dissertation(1)
公開日 2012-09-14
タイトル
タイトル Improving Semantic Similarity Measures for Word Pair Comparison
タイトル
タイトル Improving Semantic Similarity Measures for Word Pair Comparison
言語 en
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_46ec
資源タイプ thesis
著者名 MENENDEZ MORA, Raul Ernesto

× MENENDEZ MORA, Raul Ernesto

MENENDEZ MORA, Raul Ernesto

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フリガナ メヌンデス モラ, ラウル エルネスト

× メヌンデス モラ, ラウル エルネスト

メヌンデス モラ, ラウル エルネスト

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著者 MENENDEZ MORA, Raul Ernesto

× MENENDEZ MORA, Raul Ernesto

en MENENDEZ MORA, Raul Ernesto

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学位授与機関
学位授与機関名 総合研究大学院大学
学位名
学位名 博士(情報学)
学位記番号
内容記述タイプ Other
内容記述 総研大甲第1515号
研究科
値 複合科学研究科
専攻
値 17 情報学専攻
学位授与年月日
学位授与年月日 2012-03-23
学位授与年度
値 2011
要旨
内容記述タイプ Other
内容記述   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.
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