Teses / dissertações sobre o tema "Graphes relationnels"
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Jacob, Yann. "Classification dans les graphes hétérogènes et multi-relationnels avec contenu : Application aux réseaux sociaux". Paris 6, 2013. http://www.theses.fr/2013PA066494.
Texto completo da fonteThe emergence of the Web 2. 0 has seen the apparition of a large quantity of data that can easily be represented as complex graphs. There is many tasks of information analysis, prediction and retrieval on these data, while the state-of-the-art models are not adapted. In this thesis, we consider the task of node classification/labeling in complex partially labeled content networks. The applications for this task are for instance video/photo annotation in the Web 2. 0 websites, web spam detection or user labeling in social networks. The originality of our work is that we focus on two types of complex networks rarely considered in existing works: \textbf{multi-relationnal graphs} composed of multiple relation types and \textbf{heterogeneous networks} composed of multiple node types then of multiple joint labeling problems. First, we proposed two new algorithms for multi-relationnal graph labeling. These algorithms learn to weight the different relation types in the label propagation process according to their usefullness for the labeling task. They learn to combine the different relation types in an optimal manner for classification, while using the node content information. Then, we proposed an algorithm for heterogeneous graph labeling. Here, a specific problem is that each type of node has it own label set: for instance visual tags for a photo and groups for an user, then we must solve these different classification problems simultaneously using the graph structure. Our algorithm is based on the usage of a latent representation common to all node types allowing to process the different node types in an uniformized manner. Our experimental results show that this model is able to take in account the correlations between labels of different node types
Wendling, Laurent. "Segmentation floue appliquée à la recherche d'objets dans les images numériques. Graphes relationnels et reconnaissance des formes. Application à la détection d'objets dans les images sur la base d'exemples". Toulouse 3, 1997. http://www.theses.fr/1997TOU30041.
Texto completo da fonteGONZáLEZ, GóMEZ Mauricio. "Jeux stochastiques sur des graphes avec des applications à l’optimisation des smart-grids". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLN064.
Texto completo da fonteWithin the research community, there is a great interest in exploring many applications of energy grids since these become more and more important in our modern world. To properly design and implement these networks, advanced and complex mathematical tools are necessary. Two key features for their design are correctness and optimality. While these last two properties are in the core of formal methods, their effective application to energy networks remains largely unexploited. This constitutes one strong motivation for the work developed in this thesis. A special emphasis is made on the generic problem of scheduling power consumption. This is a scenario in which the consumers have a certain energy demand and want to have this demand fulfilled before a set deadline (e.g., an Electric Vehicle (EV) has to be recharged within a given time window set by the EV owner). Therefore, each consumer has to choose at each time the consumption power (by a computerized system) so that the final accumulated energy reaches a desired level. The way in which the power levels are chosen is according to a ``strategy’’ mapping at any time the relevant information of a consumer (e.g., the current accumulated energy for EV-charging) to a suitable power consumption level. The design of such strategies may be either centralized (in which there is a single decision-maker controlling all strategies of consumers), or decentralized (in which there are several decision-makers, each of them representing a consumer). We analyze both scenarios by exploiting ideas originating from formal methods, game theory and optimization. More specifically, the power consumption scheduling problem can be modelled using Markov decision processes and stochastic games. For instance, probabilities provide a way to model the environment of the electrical system, namely: the noncontrollable part of the total consumption (e.g., the non-EV consumption). The controllable consumption can be adapted to the constraints of the distribution network (e.g., to the maximum shutdown temperature of the electrical transformer), and to their objectives (e.g., all EVs are recharged). At first glance, this can be seen as a stochastic system with multi-constraints objectives. Therefore, the contributions of this thesis also concern the area of multi-criteria objective models, which allows one to pursue several objectives at a time such as having strategy designs functionally correct and robust against changes of the environment
Munch, Mélanie. "Améliorer le raisonnement dans l'incertain en combinant les modèles relationnels probabilistes et la connaissance experte". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASB011.
Texto completo da fonteThis thesis focuses on integrating expert knowledge to enhance reasoning under uncertainty. Our goal is to guide the probabilistic relations’ learning with expert knowledge for domains described by ontologies.To do so we propose to couple knowledge bases (KBs) and an oriented-object extension of Bayesian networks, the probabilistic relational models (PRMs). Our aim is to complement the statistical learning with expert knowledge in order to learn a model as close as possible to the reality and analyze it quantitatively (with probabilistic relations) and qualitatively (with causal discovery). We developped three algorithms throught three distinct approaches, whose main differences lie in their automatisation and the integration (or not) of human expert supervision.The originality of our work is the combination of two broadly opposed philosophies: while the Bayesian approach favors the statistical analysis of the given data in order to reason with it, the ontological approach is based on the modelization of expert knowledge to represent a domain. Combining the strenght of the two allows to improve both the reasoning under uncertainty and the expert knowledge
Haugeard, Jean-Emmanuel. "Extraction et reconnaissance de primitives dans les façades de Paris à l'aide d'appariement de graphes". Thesis, Cergy-Pontoise, 2010. http://www.theses.fr/2010CERG0497.
Texto completo da fonteThis last decade, modeling of 3D city became one of the challenges of multimedia search and an important focus in object recognition. In this thesis we are interested to locate various primitive, especially the windows, in the facades of Paris. At first, we present an analysis of the facades and windows properties. Then we propose an algorithm able to extract automatically window candidates. In a second part, we discuss about extraction and recognition primitives using graph matching of contours. Indeed an image of contours is readable by the human eye, which uses perceptual grouping and makes distinction between entities present in the scene. It is this mechanism that we have tried to replicate. The image is represented as a graph of adjacency of segments of contours, valued by information orientation and proximity to edge segments. For the inexact matching of graphs, we propose several variants of a new similarity based on sets of paths, able to group several contours and robust to scale changes. The similarity between paths takes into account the similarity of sets of segments of contours and the similarity of the regions defined by these paths. The selection of images from a database containing a particular object is done using a KNN or SVM classifier
Ounis, Iadh. "Un modèle d'indexation relationnel pour les graphes conceptuels fondé sur une interprétation logique". Phd thesis, Université Joseph Fourier (Grenoble), 1998. http://tel.archives-ouvertes.fr/tel-00004902.
Texto completo da fonteRoux, Bernard. "Une approche relationnelle des automates et de l'ordonnancement". Lyon 1, 2000. http://www.theses.fr/2000LYO10255.
Texto completo da fontePoulain, Rémy. "Analyse et modélisation de la diversité des structures relationnelles à l'aide de graphes multipartis". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS453.
Texto completo da fonteThere is no longer any need to prove that digital technology, the Internet and the web have led to a revolution, particularly in the way people get information. Like any revolution, it is followed by a series of issues : equal treatment of users and suppliers, ecologically sustainable consumption, freedom of expression and censorship, etc. Research needs to provide a clear vision of these stakes. Among these issues, we can talk about two phenomena : the echo chamber phenomenon and the filter bubble phenomenon. These two phenomena are linked to the lack of diversity of information visible on the Internet, and one may wonder about the impact of recommendation algorithms. Even if this is our primary motivation, we are moving away from this subject to propose a general scientific framework to analyze diversity. We find that the graph formalism is useful enough to be able to represent relational data. More precisely, we will analyze relational data with entities of different natures. This is why we chose the n-part graph formalism because this is a good way to represent a great diversity of data. Even if the first data we studied is related to recommendation algorithms (music consumption or purchase of articles on a platform) we will see over the course of the manuscript how this formalism can be adapted to other types of data (politicized users on Twitter, guests of television shows, establishment of NGOs in different States ...). There are several objectives in this study : — Mathematically define diversity indicators on the n-part graphs. — Algorithmically define how to calculate them. — Program these algorithms to make them a usable computer object. — Use these programs on quite varied data. — See the different meanings that our indicators can have. We will begin by describing the mathematical formalism necessary for our study. Then we will apply our mathematical object to basic examples to see all the possibilities that our object offers us. This will show us the importance of normalizing our indicators, and will motivate us to study random normalization. Then we will see another series of examples which will allow us to go further on our indicators, going beyond the static and tripartite side to approach graphs with more layers and depending on time. To be able to have a better vision of what the real data brings us, we will study our indicators on completely randomly generated graphs
Haugeard, Jean-Emmanuel. "Extraction et reconnaissance de primitives dans les façades de Paris à l'aide de similarités de graphes". Phd thesis, Université de Cergy Pontoise, 2010. http://tel.archives-ouvertes.fr/tel-00593985.
Texto completo da fonteRuoppolo, Domenico. "Relational graph models and Morris's observability : resource-sensitive semantic investigations on the untyped λ-calculus". Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCD069/document.
Texto completo da fonteThis thesis is a contribution to the study of Church’s untyped λ-calculus, a term rewritingsystem having the β-reduction (the formal counterpart of the idea of execution of programs) asmain rule. The focus is on denotational semantics, namely the investigation of mathematical models of the λ-calculus giving the same denotation to β-convertible λ-terms. We investigate relational semantics, a resource-sensitive semantics interpreting λ-terms as relations,with their inputs grouped together in multisets. We define a large class of relational models,called relational graph models (rgm’s), and we study them in a type/proof-theoretical way, using some non-idempotent intersection type systems. Firstly, we find the minimal and maximal λ-theories (equational theories extending -conversion) represented by the class.Then we use rgm’s to solve the full abstraction problem for Morris’s observational λ-theory,the contextual equivalence of programs that one gets by taking the β-normal forms asobservable outputs. We solve the problem in different ways. Through a type-theoretical characterization of β-normalizability, we find infinitely many fully abstract rgm’s, that wecall uniformly bottomless.We then give an exhaustive answer to the problem, by showing thatan rgm is fully abstract for Morris’s observability if and only if it is extensional (a model of ŋ-conversion) and λ-König. Intuitively an rgm is λ-König when every infinite computable tree has an infinite branch witnessed by some type of the model, where the witnessing is a property of non-well-foundedness on the type
Dib, Saker. "L'interrogation des bases de données relationnelles assistée par le graphe sémantique normalisé". Lyon 1, 1993. http://www.theses.fr/1993LYO10122.
Texto completo da fonteConde, Cespedes Patricia. "Modélisations et extensions du formalisme de l'analyse relationnelle mathématique à la modularisation des grands graphes". Paris 6, 2013. http://www.theses.fr/2013PA066654.
Texto completo da fonteGraphs are the mathematical representation of networks. Since a graph is a special type of binary relation, graph clustering (or modularization), can be mathematically modelled using the Mathematical Relational analysis. This modelling allows to compare numerous graph clustering criteria on the same type of formal representation. We give through a relational coding, the way of comparing different modularization criteria such as: Newman-Girvan, Zahn-Condorcet, Owsinski-Zadrozny, Demaine-Immorlica, Wei-Cheng, Profile Difference et Michalski-Goldberg. We introduce three modularization criteria: the Balanced Modularity, the deviation to Indetermination and the deviation to Uniformity. We identify the properties verified by those criteria and for some of those criteria, specially linear criteria, we characterize the partitions obtained by the optimization of these criteria. The final goal is to facilitate their understanding and their usefulness in some practical contexts, where their purposes become easily interpretable and understandable. Our results are tested by modularizing real networks of different sizes with the generalized Louvain algorithm
Labiod, Lazhar. "Contribution au formalisme relationnel des classifications simultanées de deux ensembles". Paris 6, 2008. http://www.theses.fr/2008PA066461.
Texto completo da fonteLabourel, Arnaud. "Partition d'arêts et représentation implicite de graphes". Bordeaux 1, 2007. http://www.theses.fr/2007BOR13490.
Texto completo da fonteLoubier, Éloïse. "Analyse et visualisation de données relationnelles par morphing de graphe prenant en compte la dimension temporelle". Toulouse 3, 2009. http://thesesups.ups-tlse.fr/2264/.
Texto completo da fonteWith word wide exchanges, companies must face increasingly strong competition and masses of information flows. They have to remain continuously informed about innovations, competition strategies and markets and at the same time they have to keep the control of their environment. The Internet development and globalization reinforced this requirement and on the other hand provided means to collect information. Once summarized and synthesized, information generally is under a relational form. To analyze such a data, graph visualization brings a relevant mean to users to interpret a form of knowledge which would have been difficult to understand otherwise. The research we have carried out results in designing graphical techniques that allow understanding human activities, their interactions but also their evolution, from the decisional point of view. We also designed a tool that combines ease of use and analysis precision. It is based on two types of complementary visualizations: statics and dynamics. The static aspect of our visualization model rests on a representation space in which the precepts of the graph theory are applied. Specific semiologies such as the choice of representation forms, granularity, and significant colors allow better and precise visualizations of the data set. The user being a core component of our model, our work rests on the specification of new types of functionalities, which support the detection and the analysis of graph structures. We propose algorithms which make it possible to target the role of the data within the structure, to analyze their environment, such as the filtering tool, the k-core, and the transitivity, to go back to the documents, and to give focus on the structural specificities. One of the main characteristics of strategic data is their strong evolution. However the statistical analysis does not make it possible to study this component, to anticipate the incurred risks, to identify the origin of a trend, and to observe the actors or terms having a decisive role in the evolution structures. With regard to dynamic graphs, our major contribution is to represent relational and temporal data at the same time; which is called graph morphing. The objective is to emphasize the significant tendencies considering the representation of a graph that includes all the periods and then by carrying out an animation between successive visualizations of the graphs attached to each period. This process makes it possible to identify structures or events, to locate them temporally, and to make a predictive reading of it. Thus our contribution allows the representation of advanced information and more precisely the identification, the analysis, and the restitution of the underlying strategic structures which connect the actors of a domain, the key words, and the concepts they use; this considering the evolution feature
Loubier, Eloïse. "Analyse et visualisation de données relationnelles par morphing de graphe prenant en compte la dimension temporelle". Phd thesis, Université Paul Sabatier - Toulouse III, 2009. http://tel.archives-ouvertes.fr/tel-00423655.
Texto completo da fonteNos travaux conduisent à l'élaboration des techniques graphiques permettant la compréhension des activités humaines, de leurs interactions mais aussi de leur évolution, dans une perspective décisionnelle. Nous concevons un outil alliant simplicité d'utilisation et précision d'analyse se basant sur deux types de visualisations complémentaires : statique et dynamique.
L'aspect statique de notre modèle de visualisation repose sur un espace de représentation, dans lequel les préceptes de la théorie des graphes sont appliqués. Le recours à des sémiologies spécifiques telles que le choix de formes de représentation, de granularité, de couleurs significatives permet une visualisation plus juste et plus précise de l'ensemble des données. L'utilisateur étant au cœur de nos préoccupations, notre contribution repose sur l'apport de fonctionnalités spécifiques, qui favorisent l'identification et l'analyse détaillée de structures de graphes. Nous proposons des algorithmes qui permettent de cibler le rôle des données au sein de la structure, d'analyser leur voisinage, tels que le filtrage, le k-core, la transitivité, de retourner aux documents sources, de partitionner le graphe ou de se focaliser sur ses spécificités structurelles.
Une caractéristique majeure des données stratégiques est leur forte évolutivité. Or l'analyse statistique ne permet pas toujours d'étudier cette composante, d'anticiper les risques encourus, d'identifier l'origine d'une tendance, d'observer les acteurs ou termes ayant un rôle décisif au cœur de structures évolutives.
Le point majeur de notre contribution pour les graphes dynamiques représentant des données à la fois relationnelles et temporelles, est le morphing de graphe. L'objectif est de faire ressortir les tendances significatives en se basant sur la représentation, dans un premier temps, d'un graphe global toutes périodes confondues puis en réalisant une animation entre les visualisations successives des graphes attachés à chaque période. Ce procédé permet d'identifier des structures ou des événements, de les situer temporellement et d'en faire une lecture prédictive.
Ainsi notre contribution permet la représentation des informations, et plus particulièrement l'identification, l'analyse et la restitution des structures stratégiques sous jacentes qui relient entre eux et à des moments donnés les acteurs d'un domaine, les mots-clés et concepts qu'ils utilisent.
Deléarde, Robin. "Configurations spatiales et segmentation pour la compréhension de scènes, application à la ré-identification". Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7020.
Texto completo da fonteModeling the spatial configuration of objects in an image is a subject that is still little discussed to date, including in the most modern computer vision approaches such as convolutional neural networks ,(CNN). However, it is an essential aspect of scene perception, and integrating it into the models should benefit many tasks in the field, by helping to bridge the “semantic gap” between the digital image and the interpretation of its content. Thus, this thesis aims to improve spatial configuration modeling ,techniques, in order to exploit it in description and recognition systems. ,First, we looked at the case of the spatial configuration between two objects, by proposing an improvement of an existing descriptor. This new descriptor called “force banner” is an extension of the histogram of the same name to a whole range of forces, which makes it possible to better describe complex configurations. We were able to show its interest in the description of scenes, by learning toautomatically classify relations in natural language from pairs of segmented objects. We then tackled the problem of the transition to scenes containing several objects and proposed an approach per object by confronting each object with all the others, rather than having one descriptor per pair. Secondly, the industrial context of this thesis led us to deal with an application to the problem of re-identification of scenes or objects, a task which is similar to fine recognition from few examples. To do so, we rely on a traditional approach by describing scene components with different descriptors dedicated to specific characteristics, such as color or shape, to which we add the spatial configuration. The comparison of two scenes is then achieved by matching their components thanks to these characteristics, using the Hungarian algorithm for instance. Different combinations of characteristics can be considered for the matching and for the final score, depending on the present and desired invariances. For each one of these two topics, we had to cope with the problems of data and segmentation. We then generated and annotated a synthetic dataset, and exploited two existing datasets by segmenting them, in two different frameworks. The first approach concerns object-background segmentation and more precisely the case where a detection is available, which may help the segmentation. It consists in using an existing global segmentation model and exploiting the detection to select the right segment, by using several geometric and semantic criteria. The second approach concerns the decomposition of a scene or an object into parts and addresses the unsupervised case. It is based on the color of the pixels, by using a clustering method in an adapted color space, such as the HSV cone that we used. All these works have shown the possibility of using the spatial configuration for the description of real scenes containing several objects, as well as in a complex processing chain such as the one we used for re-identification. In particular, the force histogram could be used for this, which makes it possible to take advantage of its good performance, by using a segmentation method adapted to the use case when processing natural images
Li, Jinpeng. "Extraction de connaissances symboliques et relationnelles appliquée aux tracés manuscrits structurés en-ligne". Phd thesis, Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00785984.
Texto completo da fonteTrouillon, Théo. "Modèles d'embeddings à valeurs complexes pour les graphes de connaissances". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM048/document.
Texto completo da fonteThe explosion of widely available relational datain the form of knowledge graphsenabled many applications, including automated personalagents, recommender systems and enhanced web search results.The very large size and notorious incompleteness of these data basescalls for automatic knowledge graph completion methods to make these applicationsviable. Knowledge graph completion, also known as link-prediction,deals with automatically understandingthe structure of large knowledge graphs---labeled directed graphs---topredict missing entries---labeled edges. An increasinglypopular approach consists in representing knowledge graphs as third-order tensors,and using tensor factorization methods to predict their missing entries.State-of-the-art factorization models propose different trade-offs between modelingexpressiveness, and time and space complexity. We introduce a newmodel, ComplEx---for Complex Embeddings---to reconcile both expressivenessand complexity through the use of complex-valued factorization, and exploreits link with unitary diagonalization.We corroborate our approach theoretically and show that all possibleknowledge graphs can be exactly decomposed by the proposed model.Our approach based on complex embeddings is arguably simple,as it only involves a complex-valued trilinear product,whereas other methods resort to more and more complicated compositionfunctions to increase their expressiveness. The proposed ComplEx model isscalable to large data sets as it remains linear in both space and time, whileconsistently outperforming alternative approaches on standardlink-prediction benchmarks. We also demonstrateits ability to learn useful vectorial representations for other tasks,by enhancing word embeddings that improve performanceson the natural language problem of entailment recognitionbetween pair of sentences.In the last part of this thesis, we explore factorization models abilityto learn relational patterns from observed data.By their vectorial nature, it is not only hard to interpretwhy this class of models works so well,but also to understand where they fail andhow they might be improved. We conduct an experimentalsurvey of state-of-the-art models, not towardsa purely comparative end, but as a means to get insightabout their inductive abilities.To assess the strengths and weaknesses of each model, we create simple tasksthat exhibit first, atomic properties of knowledge graph relations,and then, common inter-relational inference through synthetic genealogies.Based on these experimental results, we propose new researchdirections to improve on existing models, including ComplEx
Sansen, Joris. "La visualisation d’information pour les données massives : une approche par l’abstraction de données". Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0636/document.
Texto completo da fonteThe evolution and spread of technologies have led to a real explosion of information and our capacity to generate data and our need to analyze them have never been this strong. Still, the problems raised by such accumulation (storage, computation delays, diversity, speed of gathering/generation, etc. ) is as strong as the data are big, complex and varied. Information visualization,by its ability to summarize and abridge data was naturally established as appropriate approach. However, it does not solve the problem raised by Big Data. Actually, classical visualization techniques are rarely designed to handle such mass of information. Moreover, the problems raised by data storage and computation time have repercussions on the analysis system. For example,the increasing distance between the data and the analyst : the place where the data is stored and the place where the user will perform the analyses arerarely close. In this thesis, we focused on these issues and more particularly on adapting the information visualization techniques for Big Data. First of all focus on relational data : how does the existence of a relation between entity istransmitted and how to improve this transmission for hierarchical data. Then,we focus on multi-variate data and how to handle their complexity for the required computations. Finally, we present the methods we designed to make our techniques compatible with Big Data
Ayed, Rihab. "Recherche d’information agrégative dans des bases de graphes distribuées". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1305.
Texto completo da fonteIn this research, we are interested in investigating issues related to query evaluation and optimization in the framework of aggregated search. Aggregated search is a new paradigm to access massively distributed information. It aims to produce answers to queries by combining fragments of information from different sources. The queries search for objects (documents) that do not exist as such in the targeted sources, but are built from fragments extracted from the different sources. The sources might not be specified in the query expression, they are dynamically discovered at runtime. In our work, we consider data dependencies to propose a framework for optimizing query evaluation over distributed graph-oriented data sources. For this purpose, we propose an approach for the document indexing/orgranizing process of aggregated search systems. We consider information retrieval systems that are graph oriented (RDF graphs). Using graph relationships, our work is within relational aggregated search where relationships are used to aggregate fragments of information. Our goal is to optimize the access to source of information in a aggregated search system. These sources contain fragments of information that are relevant partially for the query. We aim at minimizing the number of sources to ask, also at maximizing the aggregation operations within a same source. For this, we propose to reorganize the graph database(s) in partitions, dedicated to aggregated queries. We use a semantic or strucutral clustering of RDF predicates. For structural clustering, we propose to use frequent subgraph mining algorithms, we performed for this, a comparative study of their performances. For semantic clustering, we use the descriptive metadata of RDF predicates and apply semantic textual similarity methods to calculate their relatedness. Following the clustering, we define query decomposing rules based on the semantic/structural aspects of RDF predicates
El, Abri Marwa. "Probabilistic relational models learning from graph databases". Thesis, Nantes, 2018. http://www.theses.fr/2018NANT4019/document.
Texto completo da fonteHistorically, Probabilistic Graphical Models (PGMs) are a solution for learning from uncertain and flat data, also called propositional data or attributevalue representations. In the early 2000s, great interest was addressed to the processing of relational data which includes a large number of objects participating in different relations. Probabilistic Relational Models (PRMs) present an extension of PGMs to the relational context. With the rise of the internet, numerous technological innovations and web applications are driving the dramatic increase of various and complex data. Consequently, Big Data has emerged. Several types of data stores have been created to manage this new data, including the graph databases. Recently there has been an increasing interest in graph databases to model objects and interactions. However, all PRMs structure learning use wellstructured data that are stored in relational databases. Graph databases are unstructured and schema-free data stores. Edges between nodes can have various signatures. Since, relationships that do not correspond to an ER model could be depicted in the database instance. These relationships are considered as exceptions. In this thesis, we are interested by this type of data stores. Also, we study two kinds of PRMs namely, Direct Acyclic Probabilistic Entity Relationship (DAPER) and Markov Logic Networks (MLNs). We propose two significant contributions. First, an approach to learn DAPERs from partially structured graph databases. A second approach consists to benefit from first-order logic to learn DAPERs using MLN framework to take into account the exceptions that are dropped during DAPER learning. We are conducting experimental studies to compare our proposed methods with existing approaches
García, Durán Alberto. "Learning representations in multi-relational graphs : algorithms and applications". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2271/document.
Texto completo da fonteInternet provides a huge amount of information at hand in such a variety of topics, that now everyone is able to access to any kind of knowledge. Such a big quantity of information could bring a leap forward in many areas if used properly. This way, a crucial challenge of the Artificial Intelligence community has been to gather, organize and make intelligent use of this growing amount of available knowledge. Fortunately, important efforts have been made in gathering and organizing knowledge for some time now, and a lot of structured information can be found in repositories called Knowledge Bases (KBs). A main issue with KBs is that they are far from being complete. This thesis proposes several methods to add new links between the existing entities of the KB based on the learning of representations that optimize some defined energy function. We also propose a novel application to make use of this structured information to generate questions in natural language
Ben, Letaifa Soumaya. "La théorie de l'écosystème : trois essais sur le relationnel et l'innovation dans les secteurs bancaires et des TIC (technologies de l'information et des communications)". Thèse, Paris 9, 2009. http://www.archipel.uqam.ca/2197/1/D1806.pdf.
Texto completo da fonteBresso, Emmanuel. "Organisation et exploitation des connaissances sur les réseaux d'intéractions biomoléculaires pour l'étude de l'étiologie des maladies génétiques et la caractérisation des effets secondaires de principes actifs". Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0122/document.
Texto completo da fonteThe understanding of human diseases and drug mechanisms requires today to take into account molecular interaction networks. Recent studies on biological systems are producing increasing amounts of data. However, complexity and heterogeneity of these datasets make it difficult to exploit them for understanding atypical phenotypes or drug side-effects. This thesis presents two knowledge-based integrative approaches that combine data management, graph visualization and data mining techniques in order to improve our understanding of phenotypes associated with genetic diseases or drug side-effects. Data management relies on a generic data warehouse, NetworkDB, that integrates data on proteins and their properties. Customization of the NetworkDB model and regular updates are semi-automatic. Graph visualization techniques have been coupled with NetworkDB. This approach has facilitated access to biological network data in order to study genetic disease etiology, including X-linked intellectual disability (XLID). Meaningful sub-networks of genes have thus been identified and characterized. Drug side-effect profiles have been extracted from NetworkDB and subsequently characterized by a relational learning procedure coupled with NetworkDB. The resulting rules indicate which properties of drugs and their targets (including networks) preferentially associate with a particular side-effect profile
Abadie, Lana. "Une approche "autonomic" pour la configuration d'une expérience PHE (Physique des Hautes Energies) appliquée à LHCb (Large Hadron Collider beauty)". Paris 6, 2006. http://www.theses.fr/2006PA066329.
Texto completo da fonteMillet, Pierre-Alain. "Une étude de l'intégration organisationnelle et informationnelle : application aux systèmes d'informations de type ERP". Phd thesis, INSA de Lyon, 2008. http://tel.archives-ouvertes.fr/tel-00343560.
Texto completo da fonteBagan, Guillaume. "Algorithmes et complexité des problèmes d'énumération pour l'évaluation de requêtes logiques". Phd thesis, Université de Caen, 2009. http://tel.archives-ouvertes.fr/tel-00424232.
Texto completo da fonteBresso, Emmanuel. "Organisation et exploitation des connaissances sur les réseaux d'interactions biomoléculaires pour l'étude de l'étiologie des maladies génétiques et la caractérisation des effets secondaires de principes actifs". Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00917934.
Texto completo da fontePuget, Dominique. "Aspects sémantiques dans les Systèmes de Recherche d'Informations". Toulouse 3, 1993. http://www.theses.fr/1993TOU30139.
Texto completo da fontePugeault, Florence. "Extraction dans les textes de connaissances structurées : une méthode fondée sur la sémantique lexicale linguistique". Toulouse 3, 1995. http://www.theses.fr/1995TOU30164.
Texto completo da fonteHaugeard, Jean-emmanuel. "Extraction et reconnaissance de primitives dans les façades de Paris à l'aide d'appariement de graphes". Thesis, 2010. http://www.theses.fr/2010CERG0497/document.
Texto completo da fonteThis last decade, modeling of 3D city became one of the challenges of multimedia search and an important focus in object recognition. In this thesis we are interested to locate various primitive, especially the windows, in the facades of Paris. At first, we present an analysis of the facades and windows properties. Then we propose an algorithm able to extract automatically window candidates. In a second part, we discuss about extraction and recognition primitives using graph matching of contours. Indeed an image of contours is readable by the human eye, which uses perceptual grouping and makes distinction between entities present in the scene. It is this mechanism that we have tried to replicate. The image is represented as a graph of adjacency of segments of contours, valued by information orientation and proximity to edge segments. For the inexact matching of graphs, we propose several variants of a new similarity based on sets of paths, able to group several contours and robust to scale changes. The similarity between paths takes into account the similarity of sets of segments of contours and the similarity of the regions defined by these paths. The selection of images from a database containing a particular object is done using a KNN or SVM classifier