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.

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.

No description available.

This dissertation describes WikiRank, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text. The valuation given to an entry is based not on textual similarity but instead on the links that associate entries, and an estimation of the expected frequency of visitation that would be given to each entry based on those associations in context. This estimation of relative frequency of visitation is embodied in modifications to the random walk interpretation of the PageRank algorithm. WikiRank is an effective algorithm to support natural language processing applications. It is shown to exceed the performance of previous machine learning algorithms for the task of automatic topic identification, providing results comparable to that of human annotators. Second, WikiRank is found useful for the task of recognizing text-based paraphrases on a semantic level, by comparing the distribution of attention generated by two pieces of text using the encyclopedic resource as a common reference. Finally, WikiRank is shown to have the ability to use its base of encyclopedic knowledge to recognize terms from different ontologies as describing the same thing, and thus allowing for the automatic generation of mapping links between ontologies. The conclusion of this thesis is that the "knowledge access heuristic" is valuable and that a ranking process based on a large encyclopedic resource can form the basis for an extendable general purpose mechanism capable of identifying relevant concepts by association, which in turn can be effectively utilized for enumeration and comparison at a semantic level.

Identifying topics of a textual document is useful for many purposes. We can organize the documents by topics in digital libraries. Then, we could browse and search for the documents with specific topics. By examining the topics of a document, we can quickly understand what the document is about. To augment the traditional manual way of topic tagging tasks, which is labor-intensive, solutions using computers have been developed. This dissertation describes the design and development of a topic identification approach, in this case applied to disaster events. In a sense, this study represents the marriage of research analysis with an engineering effort in that it combines inspiration from Cognitive Informatics with a practical model from Information Retrieval. One of the design constraints, however, is that the Web was used as a universal knowledge source, which was essential in accessing the required information for inferring topics from texts. Retrieving specific information of interest from such a vast information source was achieved by querying a search engine's application programming interface. Specifically, the information gathered was processed mainly by incorporating the Vector Space Model from the Information Retrieval field. As a proof of concept, we subsequently developed and evaluated a prototype tool, Xpantrac, which is able to run in a batch mode to automatically process text documents. A user interface of Xpantrac also was constructed to support an interactive semi-automatic topic tagging application, which was subsequently assessed via a usability study. Throughout the design, development, and evaluation of these various study components, we detail how the hypotheses and research questions of this dissertation have been supported and answered. We also present that our overarching goal, which was the identification of topics in a human-comparable way without depending on a large training set or a corpus, has been achieved. / Ph. D.