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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

AUTOMATIC EXTRACTION OF LOGICALLY CONSISTENT ONTOLOGIES FROM TEXT CORPORA

https://ir.soken.ac.jp/records/1503
https://ir.soken.ac.jp/records/1503
7a411a21-9f94-4627-bdb1-4b271e79bb0e
名前 / ファイル ライセンス アクション
甲1288_要旨.pdf 要旨・審査要旨 (308.8 kB)
甲1288_本文.pdf 本文 (2.1 MB)
Item type 学位論文 / Thesis or Dissertation(1)
公開日 2010-06-09
タイトル
タイトル AUTOMATIC EXTRACTION OF LOGICALLY CONSISTENT ONTOLOGIES FROM TEXT CORPORA
タイトル
タイトル AUTOMATIC EXTRACTION OF LOGICALLY CONSISTENT ONTOLOGIES FROM TEXT CORPORA
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 McCRAE, John Philip

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McCRAE, John Philip

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フリガナ マックレー, ジョン フィリップ

× マックレー, ジョン フィリップ

マックレー, ジョン フィリップ

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著者 McCRAE, John Philip

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en McCRAE, John Philip

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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1288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 2009-09-30
学位授与年度
値 2009
要旨
内容記述タイプ Other
内容記述 Ontologies provide a structured description of the concepts and terminology<br />used in a particular domain and provide valuable knowledge for a range of natu-<br />ral language processing applications. However, for many domains and languages<br />ontologies do not exist and manual creation is a difficult and resource-intensive<br />process. As such, automatic methods to extract, expand or aid the construction<br />of these resources is of significant interest.<br />  There are a number of methods for extracting semantic information about<br />how terms are related from raw text, most notably the approach of Hearst<br />[1992], who used <i>patterns</i> to extract hypernym information. This method was<br />manual and it is not clear how to automatically generate patterns, which are<br />specific to a given relationship and domain. I present a novel method for de-<br />veloping patterns based on the use of alignments between patterns. Alignment<br />works well as it is closely related to the concept of a <i>join-set</i> of patterns, which<br />minimally generalise over-fitting patterns. I show that join-sets can be viewed<br />as an reduction on the search space of patterns, while resulting in no loss of<br />accuracy. I then show the results can be combined by a <i>support vector machine</i><br />to a obtain a classifier, which can decide if a pair of terms are related. I applied<br />this to several data sets and conclude that this method produces a precise result,<br />with reasonable recall.<br />  The system I developed, like many semantic relation systems, produces only<br />a binary decision of whether a term pair is related. Ontologies have a structure,<br />that limits the forms of networks they represent. As the relation extraction is<br />generally noisy and incomplete, it is unlikely that the extracted relations will<br />match the structure of the ontology. As such I represent the structure of ontol-<br />ogy as a set of logical statements, and form a consistent ontology by finding the<br />network closest to the relation extraction system's output, which is consistent<br />with these restrictions. This gives a novel <i>NP-hard</i> optimisation problem, for<br />which I develop several algorithms. I present simple greedy approaches, and<br />branch and bound approaches, which my results show are not sufficient for this<br />problem. I then use resolution to show how this problem can be stated as an<br /><i>integer programming problem,</i> which can be efficiently solved by relaxing it to<br />a <i>linear programming problem</i>. I show that this result can efficiently solve the<br />problem, and furthermore when applied to the result of the relation extraction<br />system, this improves the quality of the extraction as well as converting it to an<br />ontological structure.
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