Data mining of maps and their automatic region-time-theme classification
SIGSPATIAL Special
Classifying Web Pages by Using Knowledge Bases for Entity Retrieval
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Ontology-based automatic classification of web documents
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Topical categorization of search results based on a domain ontology
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
An automatic approach to classify web documents using a domain ontology
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
First steps to an audio ontology-based classifier for telemedicine
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Automatic web pages hierarchical classification using dynamic domain ontologies
International Journal of Knowledge and Web Intelligence
Ontia iJADE: an intelligent ontology-based agent framework for semantic web service
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Ontology-Based classifier for audio scenes in telemedicine
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Structure based semantic measurement for information filtering agents
AOW '07 Proceedings of the Third Australasian Workshop on Advances in Ontologies - Volume 85
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In recent years, we have witnessed the continual growth inthe use of ontologies in order to provide a mechanism to enablemachine reasoning. This paper describes an automaticclassifier, which focuses on the use of ontologies for classifyingWeb pages with respect to the Dewey Decimal Classification(DDC) and Library of Congress Classification (LCC) schemes.Firstly, we explain how these ontologies can be built in amodular fashion, and mapped into DDC and LCC. Secondly,we propose the formal definition of a DDC-LCC and anontology-classification-scheme mapping. Thirdly, we explainthe way the classifier uses these ontologies to assistclassification. Finally, an experiment in which the accuracy ofthe classifier was evaluated is presented. The experiment showsthat our approach results an improved classification in terms ofaccuracy. This improvement, however, comes at a cost in a lowoverage ratio due to the incompleteness of the ontologies used. c