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Semantic Classification

Krithara, Anastasia


Keyword(s): semantic web; machine learning; document classification

Abstract: A key challenge in the semantic web is the mapping between different concepts. Many techniques for such mapping exist, but most of them induce a one-to-one mapping, which does not seem to correspond to real world problems. This project proposes a new approach, which tries to use the power of machine learning, and in particular classification algorithms, to solve the mapping task. It introduces a new semantic similarity metric which is used with semantic metadata and classification algorithms. The approach is tested in a real world dataset. Pre-processing of the dataset took place, and in particular feature selection, extraction and representation was implemented, for both content- based and semantic features. The documents of the dataset were classified using the content-based features, the semantic ones, and their combination. The results were compared and they gave us an insight of how semantic features can affect classifiers and traditional features. Notes: Anastasia Krithara, University of Bristol, Bristol, UK

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