{'fraction_non_alphanumeric': 0.011516314779270634, 'fraction_numerical': 0.0006397952655150352, 'mean_word_length': 5.4896265560165975, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Supervised Ontology-Based Document Classification Model

Ontology-based document classification relies on background knowledge exploited by ontologies to represent documents. Background knowledge is embedded in a document using the exact matching technique. The basic idea of this technique is to map a term to a concept by searching only the concept labels that explicitly occur in a document. Searching only the presence of concept labels limits the capabilities to capture and exploit the whole conceptualization involved in user information and content meanings. Therefore, to address this limitation, we propose a new document classification model based on ontologies. The proposed model uses background knowledge derived by ontologies for document representation. It associates a document with a set of concepts by not only using the exact matching technique but also by identifying and extracting new terms which can be semantically related to the concepts of ontologies. Additionally, the proposed model employs a new concept weighting technique which computes the weight of a concept using the relevance and the importance of the concept. We conducted several experiments using a real ontology and a dataset to test our proposed model. The results obtained by experiments run on 3 different classification algorithms using the baseline ontology, the improved concept vector space model by using the new concept weighting technique, and the enriched ontology, show that our proposed model achieved a considerable improvement of classification performance.