Using DL-reasoner for hierarchical multilabel classification applied to economical e-news

This work is part of a global project to develop a recommender system of economic news articles. Its objectives are threefold: (i) automatically multi-classify the economic new articles, (ii) recommend the articles by comparing the profiles of the users and the multi-classification of the articles, and (iii) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalists. In this paper we focus on the automatic multi-classification of the articles and the respective description and justification to the documentalists. While several multi-classification solutions exist they are not automatically adaptable to the problem in hands as their description of the resulting multi-classification lacks substantial correlation with the documentalists perspective. In fact, we need to consider not only the automatic classification but also the supervision of the classification and its evolution based on the documentalists supervision of the automatic classification. Accordingly, it is necessary to provide a mechanism that bridges the gap between the automatic classification mechanisms and the documentalists thesaurus, in order to support their seamless supervision of classification and of thesaurus management. Ontologies are central to our proposal, as they are used to represent and manage the thesaurus, to describe the content of the articles, and finally to automatically multi-classify them via inference process. Also, we adopt a machine learning approach for generating a prediction model for supporting the automatic classification. This paper presents a proposal for enriching the documentalist-oriented ontology with the model prediction rules, which provides the necessary capabilities to the DL reasoner for automatic multi-classification.