Natarajan Meghanathan et al. (Eds) : ICCSEA, WiMoA, SCAI, SPPR, InWeS, NECO - 2019 pp. 183-191, 2019. © CS & IT-CSCP 2019 DOI: 10.5121/csit.2019.91815 TOWARD MULTI-LABEL CLASSIFICATION USING AN ONTOLOGY FOR WEB PAGE CLASSIFICATION Yaya Traoré 1 and Sadouanouan Malo 2 and Bassolé Didier 1 and Séré Abdoulaye 2 1 University Joseph KI-ZERBO, Ouagadougou, BURKINA FASO 2 University Nazi Boni, Bobo-Dioulasso, BURKINA FASO ABSTRACT Automatic categorization of web pages has become more significant to help the search engines to provide users with relevant and quick retrieval results. In this paper, we propose a method based on Multi-label Classification (ML) using an ontology which allows the prediction of the categories of a new web page created and tagged. It uses the ontology in the learning phase as well as in the prediction phase. In the learning phase, the ontology is used to build the training set. In the prediction phase, the ontology is used to place the new pages tagged in the most specific categories. The experiment evaluation demonstrates that our proposal shows the substantial results. KEYWORDS Multi-label classification (ML), ontology, categorization, prediction. 1. INTRODUCTION Nowadays, many web platforms are used to allow collaboration between users of a community for creating and sharing knowledge. The web pages are semantically annotated. The number of web pages are continuously growing and can cover almost any information needed. However, the huge amount of web pages and the organization of these pages make the retrieval of precise and exact information more and more difficult for a user. So an efficient and accurate method for classifying this huge amount of data is very essential if the web pages are to be exploited to its full potential. There doesn’t exist any specific method to automate this task. We deal with this problem as a Multi-label (ML) classification problem [1], [12] consisting in predicting the categories of a new page according to its tags. In our context, categories are looked upon as text labels. In order to use the label relationships to build the training data, we associate ML method with ontology. An ontology [2] is used to present the domain knowledge. In this paper, we propose a novel method that uses a method of ML based on ontology to predict the categories of a new web page. Experiments are implemented to evaluate the performance of the proposed approach on the datasets of the uniprot 1 web site. The experimental results indicate that the approach has a better performance.