David Werner

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.

Precision Difference Management using a Common Sub-vector to Extend the Extended VSM Method☆

Abstract Contractors, commercial and business decision-makers need economical information to drive their decisions. The production and distribution of a press review about French regional economic actors represents a prospecting tool on partners and competitors for the businessman. Our goal is to propose a customized review for each user, thus reducing the overload of useless information. Some systems for recommending news items already exist. The usefulness of external knowledge to improve the process has already been explained in information retrieval. The systems knowledge base in- cludes the domain knowledge used during the recommendation process. Our recommender system architecture is standard, but during the indexing task, the representations of content of each article and interests of users’ profiles created are based on this domain knowledge. Articles and Profiles are semantically defined in the Knowledge base via concepts, instances and relations. This paper deals with the relevance measure, a critical sub-task in recommendation systems and relationships between relevance and similarity concepts. The Vector Space Model is a well-known model used for relevance ranking. The problematic exposed here is the utilization of the standard VSM method with our indexing method.

Semantic-Based Recommender System with Human Feeling Relevance Measure

This work presents a recommender system of economic news articles. Its objectives are threefold: (i) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalist (ii) automatically multi-classify the economic new articles and users profiles based on the domain vocabulary, and (iii) recommend the articles by comparing the multi-classification of the articles and profiles of the users. While several solutions exist to recommend news, multi-classify document and compare representations of items and profiles. They are not automatically adaptable to provide a mutual answer to previous points. Even more, existing approaches lacks substantial correlation with the human and in particular with the documentalist perspective.