Concept Mining: A Conceptual Understanding based Approach
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Due to the daily rapid growth of the information, there are considerable needs to extract and discover valuable knowledge from data sources such as the World Wide Web. Most of the common techniques in text mining are based on the statistical analysis of a term either word or phrase. These techniques consider documents as bags of words and pay no attention to the meanings of the document content. In addition, statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Therefore, there is an intensive need for a model that captures the meaning of linguistic utterances in a formal structure. The underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based model that analyzes terms on the sentence, document and corpus levels rather than the traditional analysis of document only is introduced. The concept-based model can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed model consists of concept-based statistical analyzer, conceptual ontological graph representation, concept extractor and concept-based similarity measure. The term which contributes to the sentence semantics is assigned two different weights by the concept-based statistical analyzer and the conceptual ontological graph representation. These two weights are combined into a new weight. The concepts that have maximum combined weights are selected by the concept extractor. The similarity between documents is calculated based on a new concept-based similarity measure. The proposed similarity measure takes full advantage of using the concept analysis measures on the sentence, document, and corpus levels in calculating the similarity between documents. Large sets of experiments using the proposed concept-based model on different datasets in text clustering, categorization and retrieval are conducted. The experiments demonstrate extensive comparison between traditional weighting and the concept-based weighting obtained by the concept-based model. Experimental results in text clustering, categorization and retrieval demonstrate the substantial enhancement of the quality using: (1) concept-based term frequency (tf), (2) conceptual term frequency (ctf), (3) concept-based statistical analyzer, (4) conceptual ontological graph, (5) concept-based combined model. In text clustering, the evaluation of results is relied on two quality measures, the F-Measure and the Entropy. In text categorization, the evaluation of results is relied on three quality measures, the Micro-averaged F1, the Macro-averaged F1 and the Error rate. In text retrieval, the evaluation of results relies on three quality measures, the precision at 10 documents retrieved P(10), the preference measure (bpref), and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based model is used to enhance the quality of the text clustering, categorization and retrieval.