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Based on extracting information from Wikipedia, DBpedia is a large scale knowledge base and makes this one available using Semantic Web and Linked Data principles. Thanks to crowd-sourcing, it currently covers multiples domains in multilingualism. Knowledge is obtained from different Wikipedia editions by effort of contributors around the world. Their goal is to manually generate mappings Wikipedia templates into DBpedia ontology classes (types). However, this cause makes the type inconsistency for an entity among different languages. As a result, the quality of data in DBpedia can be affected. In this paper, we present the statement of type consistency for an entity in multilingualism. As a solution for this problem, we propose a method to predict the entity type based on a novel conformity measure. We also evaluate our method based on database extracted from aggregating multilingual resources and compare it with human perception in predicting type for an entity. The experimental result shows that our method can suggest informative types and outperforms the baselines.

Online newspaper nowadays is gradually replacing the traditional one and the variety of articles on newspaper motivated the need for capturing hot topics to give Internet users a shortcut to the hot news. A hot topic always reflects the people's concern in real life and has big impact not only on community but also in business. In this paper, we proposed a novel topic detection approach by applying Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on Vector Space Model (VSM) to solve the challenge in noisy data and Pearson product-moment correlation coefficient (PMCC) on high ranking keywords to identify topics behind keywords. The proposed approach is evaluated over a dataset of ten thousand of articles and the experimental results are competitive in term of precision with other state-of-the-art methods.

Part 1: Semantic Concepts and Open Data; International audience; News on the Internet today plays an important role in helping people access daily information around the world. News aggregators are websites that collect and provide content from different sources in one location for easy viewing. However, the increasing number of news on the Internet makes it difficult for readers when they desire to access news they are concerned. One solution to this issue is based on employing recommender systems. In this research, we propose a novel method for news recommendation based on a combination of semantic similarity with content similarity between news and implement it as a feature of semantic-based news aggregators BKSport. Experimental results have shown that, a combination of both kind of similarity measures will result in better recommendation than when using either measure separately.

International audience; We first describe a multi-agent system managing a corporate memory in the form of a corporate semantic web. We then focus on a newly introduced society of agents in charge of wrapping external HTML documents that are relevant to the activities of the organization, by extracting semantic Web annotations using tailored XSLT templates.