End-to-end solution for linked open data query logs analytics

Important advances in pillar domains are derived from exploiting query-logs which represents users interest and preferences. Deep understanding of users provides useful knowledge which can influence strongly decision-making. In this work, we want to extract valuable information from Linked Open Data (LOD) query-logs. LOD logs have experienced significant growth due to the large exploitation of LOD datasets. However, exploiting these logs is a difficult task because of their complex structure. Moreover, these logs suffer from many risks related to their Quality and Provenance, impacting their trust. To tackle these issues, we start by clearly defining the ecosystem of LOD query-logs. Then, we provide an end-to-end solution to exploit these logs. At the end, real LOD logs are used and a set of experiments are conducted to validate the proposed solution.

Hate speech detection in algerian dialect using deep learning

With the proliferation of hate speech on social networks under different formats, such as abusive language, cyberbullying, and violence, etc., people have experienced a significant increase in violence, putting them in uncomfortable situations and threats. Plenty of efforts have been dedicated in the last few years to overcome this phenomenon to detect hate speech in different structured languages like English, French, Arabic, and others. However, a reduced number of works deal with Arabic dialects like Tunisian, Egyptian, and Gulf, mainly the Algerian ones. To fill in the gap, we propose in this work a complete approach for detecting hate speech on online Algerian messages. Many deep learning architectures have been evaluated on the corpus we created from some Algerian social networks (Facebook, YouTube, and Twitter). This corpus contains more than 13.5K documents in Algerian dialect written in Arabic, labeled as hateful or non-hateful. Promising results are obtained, which show the efficiency of our approach.