Extended Multidimensional Conceptual Spaces in Document Classification

TL;DRAbstract

Content based retrieval, entailing knowledge representation, can be essentially described as assessing the similarity between objects which constitute primitives as their building blocks.This thesis presents a novel approach in document classification using an Extended Multidimensional Conceptual Spaces (EMCS) framework to address problem areas that require similarity assessment.As a typical problem domain, articles on Breast, Brain, and Colon Cancers were obtained from PubMed 1 as search results of binary based queries.Since not all terms carry equal discriminatory information, only those that do were identified and treated as primitives by carrying out document pre-processing.Salient weights associated to each term thereafter were assessed statistically-computing their normalized term frequencies and inverse document frequencies.The product of these frequencies were determined as ideal features for their concept category.Example documents were also preprocessed and expressed in terms

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