The rapid growth in the number of documents available to various end users from around the world has led to a greatly increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In this approach, we try to utilize human background knowledge to help us to automatically find the best matching topic for input documents. There are several applications for this task. For example, it can be used to improve the relevancy of search engine results by categorizing the search results according to their general topic. It can also give users the ability to choose the domain which is most relevant to their needs. It can also be used for an application like a news publisher, where we want to automatically assign each news article to one of the predefined news main topics. In order to achieve this, we need to extract background knowledge in a form appropriate to this task. The thesis contributions can be summarized into two main modules. In the first module, we introduce a new approach to extract background knowledge from a human knowledge source, in the form of a knowledge repository, and store it in a well-structured and organized form, namely an ontology. We define the methodology of identifying ontological concepts, as well as defining the relations between these concepts. We use the ontology to infer the semantic similarity between documents, as well as to identify their topics. We apply our proposed approach using perhaps the best-known of the knowledge repositories, namely Wikipedia. The second module of this dissertation defines the framework for automatic document topic identification (ADTI). We present a new approach that utilizes the knowledge stored in the created ontology to automatically find the best matching topics for input documents, without the need for a training process such as in document classification. We compare ADTI to other text mining tasks by conducting several experiments to compare the performance of ADTI and its competitors, namely document clustering and document classification. Results show that our document topic identification approach outperforms several document clustering techniques. They show also that while ADTI does not require training, it nevertheless shows competitive performance with one of the state-of-the-art methods for document classification.

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Universiẗat, Diss.--Paderborn, 2005.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 86-93).

Thesis (Ph. D.)--Virginia Commonwealth University, 2007. / Prepared for: Dept. of Biostatistics. Bibliography: leaves 179-181. Also available online via the Internet.

Thesis (Ph.D. in Clinical Science) -- University of Colorado at Denver and Health Sciences Center, 2006. / Typescript. Includes bibliographical references (leaves 69-74). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;

Thesis (Ph.D. in Biostatistics) -- University of Colorado Denver, 2007. / Typescript. Includes bibliographical references (leaves 104-106). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;

Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 5863. Adviser: Lemuel Moye. Includes bibliographical references.

Issued also as Thesis (Ph. D.)--Columbia University. / Bibliography: p. 74.

Issued also as Thesis (Ph. D.)--Columbia University. / Bibliography: p. 55-56.