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We consider a number of temporal logics, some interval-based and some instant-based, and the choices that have to be made if we need to construct a computational framework for such a logic. We consider the axiomatisation of the accessibility relations of the underlying temporal structures when we are using a modal language as well as the formulation of axioms for distinguishing concepts like actions, events, processes and so on for systems using first-order languages. Finally, we briefly discuss the fields of application of temporal logics and list a number of fields that looks promising for further research. / Computer Science & Information Systems / M.Sc.(Computer Science)

A combinação de lógica e probabilidade (lógicas probabilísticas) tem sido um tópico bastante estudado nas últimas décadas. A maioria de propostas para estes formalismos pressupõem que tanto as sentenças lógicas como as probabilidades sejam especificadas por especialistas. Entretanto, a crescente disponibilidade de dados relacionais sugere o uso de técnicas de aprendizado de máquina para produzir sentenças lógicas e estimar probabilidades. Este trabalho apresenta contribuições em termos de representação de conhecimento e aprendizado. Primeiro, uma linguagem lógica probabilística de primeira ordem é proposta. Em seguida, três algoritmos de aprendizado de lógica de descrição probabilística crALC são apresentados: um algoritmo probabilístico com ênfase na indução de sentenças baseada em classificadores Noisy-OR; um algoritmo que foca na indução de inclusões probabilísticas (componente probabilístico de crALC); um algoritmo de natureza probabilística que induz sentenças lógicas ou inclusões probabilísticas. As propostas de aprendizado são avaliadas em termos de acurácia em duas tarefas: no aprendizado de lógicas de descrição e no aprendizado de terminologias probabilísticas em crALC. Adicionalmente, são discutidas aplicações destes algoritmos em processos de recuperação de informação: duas abordagens para extensão semântica de consultas na Web usando ontologias probabilísticas são discutidas. / The combination of logic and probabilities (probabilistic logics) is a topic that has been extensively explored in past decades. The majority of work in probabilistic logics assumes that both logical sentences and probabilities are specified by experts. As relational data is increasingly available, machine learning algorithms have been used to induce both logical sentences and probabilities. This work contributes in knowledge representation and learning. First, a rst-order probabilistic logic is proposed. Then, three algorithms for learning probabilistic description logic crALC are given: a probabilistic algorithm focused on learning logical sentences and based on Noisy-OR classiers; an algorithm that aims at learning probabilistic inclusions (probabilistic component of crALC) and; an algorithm that using a probabilistic setting, induces either logical sentences or probabilistic inclusions. Evaluation of these proposals has been performed in two situations: by measuring learning accuracy of both description logics and probabilistic terminologies. In addition, these learning algorithms have been applied to information retrieval processes: two approaches for semantic query extension through probabilistic ontologies are discussed.

Le Web sémantique propose un ensemble de standards et d'outils pour la formalisation et l'interopérabilité de connaissances partagées sur le Web, sous la forme d'ontologies. Les ontologies biomédicales et les données associées constituent de nos jours un ensemble de connaissances complexes, hétérogènes et interconnectées, dont l'analyse est porteuse de grands enjeux en santé, par exemple dans le cadre de la pharmacovigilance. On proposera dans cette thèse des méthodes permettant d'utiliser ces ontologies biomédicales pour étendre les possibilités d'un processus de fouille de données, en particulier, permettant de faire cohabiter et d'exploiter les connaissances de plusieurs ontologies biomédicales. Les travaux de cette thèse concernent dans un premier temps une méthode fondée sur les structures de patrons, une extension de l'analyse formelle de concepts pour la découverte de co-occurences de événements indésirables médicamenteux dans des données patients. Cette méthode utilise une ontologie de phénotypes et une ontologie de médicaments pour permettre la comparaison de ces événements complexes, et la découverte d'associations à différents niveaux de généralisation, par exemple, au niveau de médicaments ou de classes de médicaments. Dans un second temps, on utilisera une méthode numérique fondée sur des mesures de similarité sémantique pour la classification de déficiences intellectuelles génétiques. On étudiera deux mesures de similarité utilisant des méthodes de calcul différentes, que l'on utilisera avec différentes combinaisons d'ontologies phénotypiques et géniques. En particulier, on quantifiera l'influence que les différentes connaissances de domaine ont sur la capacité de classification de ces mesures, et comment ces connaissances peuvent coopérer au sein de telles méthodes numériques. Une troisième étude utilise les données ouvertes liées ou LOD du Web sémantique et les ontologies associées dans le but de caractériser des gènes responsables de déficiences intellectuelles. On utilise ici la programmation logique inductive, qui s'avère adaptée pour fouiller des données relationnelles comme les LOD, en prenant en compte leurs relations avec les ontologies, et en extraire un modèle prédictif et descriptif des gènes responsables de déficiences intellectuelles. L'ensemble des contributions de cette thèse montre qu'il est possible de faire coopérer avantageusement une ou plusieurs ontologies dans divers processus de fouille de données / 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. 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. A 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. Finally, 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. These contributions illustrates the possibility of having several ontologies cooperate to improve various data mining processes

A comunicação multilíngue é uma tarefa cada vez mais imperativa no cenário atual de grande disseminação de informações em diversas línguas. Nesse contexto, são de grande relevância os sistemas de tradução automática, que auxiliam tal comunicação, automatizando-a. Apesar de ser uma área de pesquisa bastante antiga, a Tradução Automática ainda apresenta muitos problemas. Um dos principais problemas é a ambigüidade lexical, ou seja, a necessidade de escolha de uma palavra, na língua alvo, para traduzir uma palavra da língua fonte quando há várias opções de tradução. Esse problema se mostra ainda mais complexo quando são identificadas apenas variações de sentido nas opções de tradução. Ele é denominado, nesse caso, \"ambigüidade lexical de sentido\". Várias abordagens têm sido propostas para a desambiguação lexical de sentido, mas elas são, em geral, monolíngues (para o inglês) e independentes de aplicação. Além disso, apresentam limitações no que diz respeito às fontes de conhecimento que podem ser exploradas. Em se tratando da língua portuguesa, em especial, não há pesquisas significativas voltadas para a resolução desse problema. O objetivo deste trabalho é a proposta e desenvolvimento de uma nova abordagem de desambiguação lexical de sentido, voltada especificamente para a tradução automática, que segue uma metodologia híbrida (baseada em conhecimento e em córpus) e utiliza um formalismo relacional para a representação de vários tipos de conhecimentos e de exemplos de desambiguação, por meio da técnica de Programação Lógica Indutiva. Experimentos diversos mostraram que a abordagem proposta supera abordagens alternativas para a desambiguação multilíngue e apresenta desempenho superior ou comparável ao do estado da arte em desambiguação monolíngue. Adicionalmente, tal abordagem se mostrou efetiva como mecanismo auxiliar para a escolha lexical na tradução automática estatística / Crosslingual communication has become a very imperative task in the current scenario with the increasing amount of information dissemination in several languages. In this context, machine translation systems, which can facilitate such communication by providing automatic translations, are of great importance. Although research in Machine Translation dates back to the 1950\'s, the area still has many problems. One of the main problems is that of lexical ambiguity, that is, the need for lexical choice when translating a source language word that has several translation options in the target language. This problem is even more complex when only sense variations are found in the translation options, a problem named \"sense ambiguity\". Several approaches have been proposed for word sense disambiguation, but they are in general monolingual (for English) and application-independent. Moreover, they have limitations regarding the types of knowledge sources that can be exploited. Particularly, there is no significant research aiming to word sense disambiguation involving Portuguese. The goal of this PhD work is the proposal and development of a novel approach for word sense disambiguation which is specifically designed for machine translation, follows a hybrid methodology (knowledge and corpus-based), and employs a relational formalism to represent various kinds of knowledge sources and disambiguation examples, by using Inductive Logic Programming. Several experiments have shown that the proposed approach overcomes alternative approaches in multilingual disambiguation and achieves higher or comparable results to the state of the art in monolingual disambiguation. Additionally, the approach has shown to effectively assist lexical choice in a statistical machine translation system

La biologie des systèmes intègre données et connaissances par des méthodes bioinformatiques, afin de mieux appréhender la physiologie des organismes. Une problématique est l’applicabilité de ces techniques aux organismes non modèles, au centre de plus en plus d’études, grâce aux avancées de séquençage et à l’intérêt croissant de la recherche sur les microbiotes. Cette thèse s’intéresse à la modélisation du métabolisme par des réseaux, et de sa fonctionnalité par diverses sémantiques basées sur les graphes et les contraintes stoechiométriques. Une première partie présente des travaux sur la complétion de réseaux métaboliques pour les organismes non modèles. Une méthode basée sur les graphes est validée, et une seconde, hybride, est développée, en programmation par ensembles réponses (ASP). Ces complétions sont appliquées à des réseaux métaboliques d’algues en biologie marine, et étendues à la recherche de complémentarité métabolique entre Ectocarpus siliculosus et une bactérie symbiotique. En s’appuyant sur les méthodes de complétion, la seconde partie de la thèse vise à proposer et implémenter une sélection de communautés à l’échelle de grands microbiotes. Une approche en deux étapes permet de suggérer des symbiotes pour l’optimisation d’un objectif donné. Elle supporte la modélisation des échanges et couvre tout l’espace des solutions. Des applications sur le microbiote intestinal humain et la sélection de bactéries pour une algue brune sont présentées. Dans l’ensemble, cette thèse propose de modéliser, développer et appliquer des méthodes reposant sur des sémantiques de graphe pour élaborer des hypothèses sur le métabolisme des organismes. / Systems biology relies on computational biology to integrate knowledge and data, for a better understanding of organisms’ physiology. Challenges reside in the applicability of methods and tools to non-model organisms, for instance in marine biology. Sequencing advances and the growing importance of elucidating microbiotas’ roles, have led to an increased interest into these organisms. This thesis focuses on the modeling of the metabolism through networks, and of its functionality using graphs and constraints semantics. In particular, a first part presents work on gap-filling metabolic networks in the context of non-model organisms. A graph-based method is benchmarked and validated and a hybrid one is developed using Answer Set Programming (ASP) and linear programming. Such gap-filling is applied on algae and extended to decipher putative interactions between Ectocarpus siliculosus and a symbiotic bacterium. In this direction, the second part of the thesis aims at proposing formalisms and implementation of a tool for selecting and screening communities of interest within microbiotas. It enables to scale to large microbiotas and, with a two-step approach, to suggest symbionts that fit the desired objective. The modeling supports the computation of exchanges, and solving can cover the whole solution space. Applications are presented on the human gut microbiota and the selection of bacterial communities for a brown alga. Altogether, this thesis proposes modeling, software and biological applications using graph-based semantics to support the elaboration of hypotheses for elucidating the metabolism of organisms.

Application of neural networks in deep learning is rapidly growing due to their ability to outperform other machine learning algorithms in different kinds of problems. But one big disadvantage of deep neural networks is its internal logic to achieve the desired output or result that is un-understandable and unexplainable. This behavior of the deep neural network is known as “black box”. This leads to the following questions: how prevalent is the black box problem in the research literature during a specific period of time? The black box problems are usually addressed by socalled rule extraction. The second research question is: what rule extracting methods have been proposed to solve such kind of problems? To answer the research questions, a systematic literature review was conducted for data collection related to topics, the black box, and the rule extraction. The printed and online articles published in higher ranks journals and conference proceedings were selected to investigate and answer the research questions. The analysis unit was a set of journals and conference proceedings articles related to the topics, the black box, and the rule extraction. The results conclude that there has been gradually increasing interest in the black box problems with the passage of time mainly because of new technological development. The thesis also provides an overview of different methodological approaches used for rule extraction methods.

<p>This thesis presents the implementation of a knowledge base system for rough sets [Paw92]within the logic programming framework. The combination of rough set theory with logic programming is a novel approach. The presented implementation serves as a prototype system for the ideas presented in [VDM03a, VDM03b]. The system is available at "http://www.ida.liu.se/rkbs". </p><p>The presented language for describing knowledge in the rough knowledge base caters for implicit definition of rough sets by combining different regions (e.g. upper approximation, lower approximation, boundary) of other defined rough sets. The rough knowledge base system also provides methods for querying the knowledge base and methods for computing quantitative measures. </p><p>We test the implemented system on a medium sized application example to illustrate the usefulness of the system and the incorporated language. We also provide performance measurements of the system.</p>

In this dissertation we lay the groundwork of automated analysis of extended feature models with constraint programming. Among different proposals, feature modeling has proven to be very effective for modeling and managing variability in Software Product Lines. However, industrial experiences showed that feature models often grow too large with hundreds of features and complex cross-tree relationships, which necessitates automated analysis support. To address this issue we present a mapping from extended feature models, which may include complex feature-feature, feature-attribute and attribute-attribute cross-tree relationships as well as global constraints, to constraint logic programming over finite domains. Then, we discuss the effects of including complex feature attribute relationships on various analysis operations defined on the feature models. As new types of variability emerge due to the inclusion of feature attributes in cross-tree relationships, we discuss the necessity of reformulation of some of the analysis operations and suggest a revised understanding for some other. We also propose new analysis operations arising due to the nature of the new variability introduced. Then we propose a transformation from extended feature models to basic/cardinality-based feature models that may be applied under certain circumstances and enables using SAT or BDD solvers in automated analysis of extended feature models. Finally, we discuss the role of the context information in feature modeling, and propose to use context information in staged configuration of feature-models.

Αντικείμενο της εργασίας αυτής αποτελεί η χρήση της γλώσσας λογικού προγραμματισμού Prolog για τη συντακτική ανάλυση (parsing) παραδειγμάτων ρηματικής φράσης, ακολουθώντας τις τελευταίες εξελίξεις στο χώρο της γενετικής γραμματικής (Μινιμαλιστικό Πρόγραμμα). Στην προσπάθεια πλήρους περιγραφής του προβλήματος, όπως απαιτεί ο λογικός προγραμματισμός, στο επίκεντρο τοποθετείται η διεπαφή σύνταξης – σημασιολογίας. Γίνεται αναφορά σε θεωρητικά προβλήματα όπως η διάκριση μεταξύ ανεργαστικών και μεταβατικών ρημάτων και η φύση των ρημάτων καιρού (weather verbs), και προτείνονται τρόποι ερμηνείας και διαχείρισης των παραπάνω προβλημάτων στο περιβάλλον της Prolog. / The purpose of this study is to examine the use of the logic programming language Prolog as a parser for VPs within the generative grammar/Minimalist Program framework. We focus on the syntax- semantics interface and try to address issues such as the differences between unergative- transitive verbs and the structure of weather verbs within the Prolog environment.