WEKO3
アイテム
AUTOMATIC EXTRACTION OF LOGICALLY CONSISTENT ONTOLOGIES FROM TEXT CORPORA
https://ir.soken.ac.jp/records/1503
https://ir.soken.ac.jp/records/15037a411a21-9f94-4627-bdb1-4b271e79bb0e
名前 / ファイル | ライセンス | アクション |
---|---|---|
要旨・審査要旨 (308.8 kB)
|
||
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
× McCRAE, John Philip |
|||||
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 |
|||||
著者 |
McCRAE, John Philip
× McCRAE, John Philip |
|||||
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. | |||||
所蔵 | ||||||
値 | 有 | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |