An ontology-based chatbot for crises management: use case coronavirus

Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term "disaster management" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface

Alignment of Protein-Protein Interaction Networks

PPI network alignment aims to find topological and functional similarities between networks of different species. Several alignment approaches have been proposed. Each of these approaches relies on a different alignment method and uses different biological information during the alignment process such as the topological structure of the networks and the sequence similarities between the proteins, but less of them integrate the functional similarities between proteins. In this context, we present our algorithm PPINA (Protein-Protein Interaction Network Aligner), which is an extension of the NETAL algorithm. The latter aligns two networks based on the sequence, functional and network topology similarity of the proteins. PPINA has been tested on real PPI networks. The results show that PPINA has outperformed other alignment algorithms where it provides biologically meaningful results.Comment: 57 pages, in French, 9 figure

D\'etermination Automatique des Fonctions d'Appartenance et Interrogation Flexible et Coop\'erative des Bases de Donn\'ees

Flexible querying of DB allows to extend DBMS in order to support imprecision and flexibility in queries. Flexible queries use vague and imprecise terms which have been defined as fuzzy sets. However, there is no consensus on memberships functions generation. Most of the proposed methods require expert intervention. This thesis is devised in two parts. In the first part, we propose a clustering based approach for automatic and incremental membership functions generation. We have proposed the clustering method CLUSTERDB* which evaluates clustering quality underway clusters generation. Moreover, we propose incremental updates of partitions and membership functions after insertion or deletion of a new object. The second part of this thesis uses these functions and Formal Concepts Analysis in flexible and cooperative querying. In case of empty answers, we formally detect the failure reasons and we generate approximative queries with their answers. These queries help the user to formulate new queries having answers. The different proposed approaches are implemented and experimented with several databases. The experimentation results are encouraging.Comment: 133 pages, in French. 22 figures PhD thesi

Motifs corr\'el\'es rares : Caract\'erisation et nouvelles repr\'esentations concises

Recently, rare pattern mining proves to be of added-value in different data mining applications since these patterns allow conveying knowledge on rare and unexpected events. However, the extraction of rare patterns suffers from two main limits, namely the large number of mined patterns in real-life applications, as well as the low informativeness quality of several rare patterns. In this situation, we propose to use the correlation measure, bond, in the mining process in order to only retain those rare patterns having a certain degree of correlation between their respective items. A characterization of the resulting set, of rare correlated patterns, is then carried out based on the study of constraints of distinct types induced by the rarity and the correlation. In addition, based on the equivalence classes associated to a closure operator dedicated to the bond measure, we propose concise representations of rare correlated patterns. We then design a new algorithm CRP_Miner dedicated to the extraction of the whole set of rare correlated patterns. We also introduce the CRPR_Miner algorithm allowing an efficient extraction of the proposed concise representations. In addition, we design two other algorithms which allow to us the query and the regeneration of the whole set of rare correlated patterns. The carried out experimental studies show the effectiveness of the algorithm CRPR_Miner and prove the compactness rate offered by the proposed concise representations.Comment: in French. Master's thesis 201