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DTSTART;TZID=Europe/Paris:20181109T140000
DTEND;TZID=Europe/Paris:20181109T150000
DTSTAMP:20181107T131026Z
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SUMMARY:PhD Defense : Gabin Personeni
DESCRIPTION:Gabin Personeni (Orpailleur) will defend his thesis on Friday\, November 9th at 2pm in room A008. \nHis thesis is entitled « Contribution of domain ontologies for knowledge discovery in biomedical data ». \n \nThe jury will be composed of the following 8 members :\n\nRapporteurs\n-Olivier Dameron\, Maître de Conférences à l’Université de Rennes 1\n-Céline Rouveirol\, Professeur à l’Université Paris 13\nExaminateurs\n-Jérôme Azé\, Professeur à l’Université de Montpellier\n-Anne Boyer\, Professeur à l’Université de Lorraine\n-Adrien Coulet\, Maître de Conférences à l’Université de Lorraine\n-Marie-Dominique\, Chargée de Recherches\, CNRS\nInvités\n-Michel Dumontier\, Distinguished Professor\, Maastricht University\n-Malika Smaïl-Tabbone\,  Maître de Conférences à l’Université de Lorraine\n\n\n\n\nAbstract\n\n\n   The semantic Web proposes standards and tools to formalize and share knowledge on the Web\, in the form of ontologies. Biomedical ontologies and associated data represents a vast collection of complex\, heterogeneous and linked knowledge. The analysis of such knowledge presents great opportunities in healthcare\, for instance in pharmacovigilance. This thesis explores several ways to make use of this biomedical knowledge in the data mining step of a knowledge discovery process. In particular\, we propose three methods in which several ontologies cooperate to improve data mining results.\n A first contribution of this thesis describes a method based on pattern structures\, an extension of formal concept analysis\, to extract associations between adverse drug events from patient data. In this context\, a phenotype ontology and a drug ontology cooperate to allow a semantic comparison of these complex adverse events\, and leading to the discovery of associations between such events at varying degrees of generalization\, for instance\, at the drug or drug class level.\nA second contribution uses a numeric method based on semantic similarity measures to classify different types of genetic intellectual disabilities\, characterized by both their phenotypes and the functions of their linked genes. We study two different similarity measures\, applied with different combinations of phenotypic and gene function ontologies. In particular\, we investigate the influence of each domain of knowledge represented in each ontology on the classification process\, and how they can cooperate to improve that process.\nFinally\, a third contribution uses the data component of the semantic Web\, the Linked Open Data (LOD)\, together with linked ontologies\, to characterize genes responsible for intellectual deficiencies. We use Inductive Logic Programming\, a suitable method to mine relational data such as LOD while exploiting domain knowledge from ontologies by using reasoning mechanisms. Here\, ILP allows to extract from LOD and ontologies a descriptive and predictive model of genes responsible for intellectual disabilities.\nThese contributions illustrates the possibility of having several ontologies cooperate to improve various data mining processes.
URL:https://www.loria.fr/event/phd-defense-gabin-personeni/
CATEGORIES:Soutenance
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181120T134500
DTEND;TZID=Europe/Paris:20181120T150000
DTSTAMP:20181114T160716Z
CREATED:20181114T160716Z
LAST-MODIFIED:20181114T160716Z
UID:6208-1542721500-1542726000@www.loria.fr
SUMMARY:PhD Defense : Amaury L'Huillier
DESCRIPTION:Amaury L’huillier (Kiwi) will defend his thesis on Tuesday\, November 20th at 1.45pm in room C005. \nHis thesis is entitled « Modeling diversity over time to understand user context in recommender systems ». \n \nComposition du jury : \nRapporteurs:– Max Chevalier : Professeur\, Université Paul Sabatier– Catherine Berrut : Professeur\, Université Grenoble AlpesExaminateurs: \n– Miguel Couceiro : Professeur\, Université de Lorraine– Francis Rousseaux : Professeur\, Université de Reims Champagne-Ardenne\nDirecteurs de thèse :– Anne Boyer : Professeur\, Université de Lorraine– Sylvain Castagnos : Maître de conférences\, Université de Lorraine\n\n\nModéliser la diversité au cours du temps pour comprendre le contexte de l’utilisateur dans les systèmes de recommandation.\n\n\nRÉSUMÉ—————\n\nLes systèmes de recommandation se sont imposés comme étant des outils indispensables face à une quantité de données qui ne cesse chaque jour de croître depuis l’avènement d’Internet. Leur objectif est de proposer aux utilisateurs des items susceptibles de les intéresser sans que ces derniers n’aient besoin d’agir pour les obtenir. Après s’être majoritairement focalisés sur la précision de la prédiction d’intérêt\, ces systèmes ont évolué pour prendre en compte d’autres critères dans leur processus de recommandation\, tels que les facteurs humains inhérents à la prise de décision\, afin d’améliorer la qualité et l’utilité des recommandations. Cependant\, la prise en compte de certains facteurs humains tels que la diversité et le contexte demeure critiquable. Alors que le contexte des utilisateurs est inféré sur la base d’informations collectées à l’insu de leur vie privée\, la prise en compte de la diversité est quant à elle réduite à une dimension qu’un système se doit de maximiser. Or\, certains travaux récents démontrent que la diversité correspond à un besoin évoluant dynamiquement au cours du temps\, et dont la proportion à insuffler dans les recommandations est dépendante de la tâche effectuée (i.e du contexte). Partant du postulat inverse selon lequel l’analyse de l’évolution de la diversité au cours du temps permet de définir le contexte de l’utilisateur\, nous proposons dans ce manuscrit une nouvelle approche de modélisation contextuelle basée sur la diversité. En effet\, nous soutenons qu’une variation de diversité remarquable peut être la conséquence d’un changement de contexte et qu’il faut alors adapter la stratégie de recommandation en conséquence. Nous présentons la première approche de la littérature permettant de modéliser en temps réel l’évolution de la diversité\, ainsi qu’une nouvelle famille de contextes dits implicites n’exploitant aucune donnée sensible. La possibilité de remplacer les contextes traditionnels (explicites) par les contextes implicites est confirmée de plusieurs manières. Premièrement\, nous démontrons sur deux corpus issus d’applications réelles qu’il existe un fort recouvrement entre les changements de contextes explicites et les changements de contextes implicites. Deuxièmement\, une étude utilisateur impliquant de nombreux participants nous permet de démontrer l’existence de liens entre les contextes explicites et les caractéristiques des items consultés dans ces derniers. Fort de ces constats et du potentiel offert par nos modèles\, nous présentons également plusieurs approches de recommandation et de prise en compte des besoins des utilisateurs.\n\nMots-clés: Systèmes de recommandation\, diversité\, contexte\, vie privée\n\n\n\n\nAbstract\n————\n\nRecommender Systems (RS) have become essential tools to deal with an endless increasing amount of data available on the Internet. Their goal is to provide items that may interest users before they have to find them by themselves. After being exclusively focused on the precision of users’ interests prediction task\, RS had to evolve by taking into account other criteria like human factors involved in the decision-making process while computing recommendations\, so as to improve their quality and usefulness of recommendations. Nevertheless\, the way some human factors\, such as context and diversity needs\, are managed remains open to criticism. While context-aware recommendations relies on exploiting data that are collected without any consideration for users’ privacy\, diversity has been coming down to a dimension which has to be maximized. However recent studies demonstrate that diversity corresponds to a need which evolves dynamically over time. In addition\, the optimal amount of diversity to provide in the recommendations depends on the on-going task of users (i.e their contexts). Thereby\, we argue that analyzing the evolution of diversity over time would be a promising way to define a user’s context\, under the condition that context is now defined by item attributes. Indeed\, we support the idea that a sudden variation of diversity can reflect a change of user’s context which requires to adapt the recommendation strategy. We present in this manuscript the first approach to model the evolution of diversity over time and a new kind of context\, called “implicit contexts”\, that are respectful of privacy (in opposition to explicit contexts). We confirm the benefits of implicit contexts compared to explicit contexts from several points of view. As a first step\, using two large music streaming datasets we demonstrate that explicit and implicit context changes are highly correlated. As a second step\, a user study involving many participants allowed us to demonstrate the links between the explicit contexts and the characteristics of the items consulted in the meantime. Based on these observations and the advantages offered by our models\, we also present several approaches to provide privacy-preserving context-aware recommendations and to take into account user’s needs.\n\nKeywords: Recommender Systems\, diversity\, context\, privacy
URL:https://www.loria.fr/event/phd-defense-amaury-lhuillier/
CATEGORIES:Soutenance
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DTSTART;TZID=Europe/Paris:20181128T103000
DTEND;TZID=Europe/Paris:20181128T120000
DTSTAMP:20181127T083232Z
CREATED:20181114T160120Z
LAST-MODIFIED:20181127T083232Z
UID:6206-1543401000-1543406400@www.loria.fr
SUMMARY:PhD Defense : Yacine Abboud
DESCRIPTION:Yacine Abboud (Kiwi) will defend his thesis on Wednesday\, November 28th in room A008 at 10.30 am. \nHis thesis is entitled « Pattern mining: between accessibility and robustness« . \n \nDissertation committee:\n——————————— \nReviewers:\n– Sandra BRINGAY : Full Professor\, University of Montpellier 3\n– Omar BOUCELMA : Full Professor\, University of Aix-Marseille \nExaminers: \n– Vincent GUIGUE: Associate Professor\, UPMC – LIP6\n– François CHAROY : Full Professor\, University of Lorraine \n\nThesis supervisors:\n– Anne BOYER : Full Professor\, University of Lorraine\n– Armelle BRUN : Associate Professor\, University of Lorraine \nAbstract \n———— \nInformation now occupies a central place in our daily lives\, it is both ubiquitous and easy to access. Yet extracting information from data is often an inaccessible process. Indeed\, even though data mining methods are now accessible to all\, the results of these mining are often complex to obtain and exploit for the user. Pattern mining combined with the use of constraints is a very promising direction of the literature to both improve the efficiency of the mining and make its results more apprehensible to the user. However\, the combination of constraints desired by the user is often problematic because it does not always fit with the characteristics of the searched data such as noise. In this thesis\, we propose two new constraints and an algorithm to overcome this issue. The robustness constraint allows to mine noisy data while preserving the added value of the contiguity constraint. The extended closedness constraint improves the apprehensibility of the set of extracted patterns while being more noise-resistant than the conventional closedness constraint. The C3Ro algorithm is a generic sequential pattern mining algorithm that integrates many constraints\, including the two new constraints that we have introduced\, to provide the user the most efficient mining possible while reducing the size of the set of extracted patterns. C3Ro competes with the best pattern mining algorithms in the literature in terms of execution time while consuming significantly less memory. C3Ro has been experienced in extracting competencies from web-based job postings. \nKeywords: data mining\, pattern mining\, closed contiguous sequential pattern mining\, constraints\, noise-resistant
URL:https://www.loria.fr/event/phd-defense-yacine-abboud/
CATEGORIES:Soutenance
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