Ontology-Based Mapping for Automated Document Management

Abstract

Document clustering is crucial to automated document management, especially for the fast-growing volume of textual documents available digitally. Traditional lexicon-based approaches depend on document content analysis and measure overlap of the feature vectors representing different documents, which cannot effectively address word mismatch or ambiguity problems. Alternative query expansion and local context discovery approaches are developed but suffer from limited efficiency and effectiveness, because the large number of expanded terms create noise and increase the dimensionality and complexity of the overall feature space. Several techniques extend lexicon-based analysis by incorporating latent semantic indexing but produce less comprehensible clustering results and questionable performance. We instead propose a concept-based document representation and clustering (CDRC) technique and empirically examine its effectiveness using 433 articles concerning information systems and technology, randomly selected from a popular digital library. Our evaluation includes two widely used benchmark techniques and shows that CDRC outperforms them. Overall, our results reveal that clustering documents at an ontology-based, concept-based level is more effective than techniques using lexicon-based document features and can generate more comprehensible clustering results.