Employing Ontology Enrichment Algorithm in Classifying Biomedical Text Abstracts

The application of text classification systems on biomedical literature aims to select articles relevant to a specific issue from large corpora. As the amount of online biomedical literature grows, the task of finding relevant information becomes very complicated, due to the difficulties in browsing and searching the relevant information through the web. Ontology is useful for organizing and navigating the Web sites and also for improving the accuracy of Web searches. It provides a shared understanding of domain, to overcome differences in terminology such as synonym, term variants and terms ambiguity. However, one of the problems raised in ontology is the maintenance of these bases of concepts. Therefore, we investigate and propose an ontology enrichment algorithm as one of the methods to modify an existing ontology. In this research, we present a new ontology enrichment algorithm for assigning or associating each concept in the training ontology with the relevant and informative features from biomedical information sources. Experiments are conducted to extract and select the meaningful features from different information sources such as the OHSUMED dataset, Medical Subject Heading (MeSH) terms and heart disease glossaries. Then, we expand these features into the training ontology. Finally, we evaluate the performance of our proposed ontology enrichment algorithm in classifying biomedical text

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