Linked Data (LD) follows the web in providing low barriers to publication, and in deploying web-scale keyword search as a central way of identifying relevant data. As in the web, searchesinitially identify results in broadly the form in which they were published, and the published form may be provided to the user as the result of a search. This will be satisfactory in some cases, but the diversity of publishers means that the results of the search may be obtained from many different sources, and described in many different ways. As such, there seems to bean opportunity to add value to search results by providing userswith an integrated representation that brings together features from different sources. This involves an on-the-fly and automated data integration process being applied to search results, which raises the question as to what technologies might bemost suitable for supporting the integration of LD searchresults. In this thesis we take the view that the problem of integrating LD search results is best approached by assimilating different forms ofevidence that support the integration process. In particular, thisdissertation shows how Statistical Relational Learning (SRL) formalisms (viz., Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL)) can beexploited to assimilate different sources of evidence in a principledway and to beneficial effect for users. Specifically, in this dissertation weconsider syntactic evidence derived from LD search results and from matching algorithms, semantic evidence derived from LD vocabularies, and user evidence,in the form of feedback. This dissertation makes the following key contributions: (i) a characterisation of key features of LD search results that are relevant to their integration, and a description of some initial experiences in the use of MLN for interpreting search results; (ii)a PSL rule-base that models the uniform assimilation of diverse kinds of evidence;(iii) an empirical evaluation of how the contributed MLN and PSL approaches perform in terms of their ability to infer a structure for integrating LD search results;and (iv) concrete examples of how populating such inferred structures for presentation to the end user is beneficial, as well as guiding the collection of feedbackwhose assimilation further improves search results presentation.

As the requirements for software systems increase, their size, complexity and functionality consequently increases as well. This has a direct impact on the complexity of numerous artifacts related to the system such as specification, design, implementation and, testing models. Furthermore, as the software market becomes more and more competitive, the need for software products that are of high quality and require the least monetary, time and human resources for their development and maintenance becomes evident. Therefore, it is important that project managers and software engineers are given the necessary tools to obtain a more holistic and accurate perspective of the status of their projects in order to early identify potential risks, flaws, and quality issues that may arise during each stage of the software project life cycle. In this respect, practitioners and academics alike have recognized the significance of investigating new methods for supporting software management operations with respect to large software projects. The main target of this M.A.Sc. thesis is the design of a framework in terms of, first, a reference architecture for mining and analyzing of software project data repositories according to specific objectives and analytic knowledge, second, the techniques to model such analytic knowledge and, third, a reasoning methodology for verifying or denying hypotheses related to analysis objectives. Such a framework could assist project managers, team leaders and development teams towards more accurate prediction of project traits such as quality analysis, risk assessment, cost estimation and progress evaluation. More specifically, the framework utilizes goal models to specify analysis objectives as well as, possible ways by which these objectives can be achieved. Examples of such analysis objectives for a project could be to yield, high code quality, achieve low production cost or, cope with tight delivery deadlines. Such goal models are consequently transformed into collections of Markov Logic Network rules which are then applied to the repository data in order to verify or deny with a degree of probability, whether the particular project objectives can be met as the project evolves. The proposed framework has been applied, as a proof of concept, on a repository pertaining to three industrial projects with more that one hundred development tasks.

Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from these noisy structured/relational data. Markov logic networks (MLNs), sets of weighted first-order logic formulae, are a simple but powerful SRL formalism that generalizes both first-order logic and Markov networks. MLNs have been successfully applied to a variety of real-world problems ranging from extraction knowledge from text to visual event recognition. Most of the existing learning algorithms for MLNs are in the generative setting: they try to learn a model that is equally capable of predicting the values of all variables given an arbitrary set of evidence; and they do not scale to problems with thousands of examples. However, many real-world problems in structured/relational data are discriminative--where the variables are divided into two disjoint sets input and output, and the goal is to correctly predict the values of the output variables given evidence data about the input variables. In addition, these problems usually involve data that have thousands of examples. Thus, it is important to develop new discriminative learning methods for MLNs that are more accurate and more scalable, which are the topics addressed in this thesis. First, we present a new method that discriminatively learns both the structure and parameters for a special class of MLNs where all the clauses are non-recursive ones. Non-recursive clauses arise in many learning problems in Inductive Logic Programming. To further improve the predictive accuracy, we propose a max-margin approach to learning weights for MLNs. Then, to address the issue of scalability, we present CDA, an online max-margin weight learning algorithm for MLNs. Ater [sic] that, we present OSL, the first algorithm that performs both online structure learning and parameter learning. Finally, we address an issue arising in applying MLNs to many real-world problems: learning in the presence of many hard constraints. Including hard constraints during training greatly increases the computational complexity of the learning problem. Thus, we propose a simple heuristic for selecting which hard constraints to include during training. Experimental results on several real-world problems show that the proposed methods are more accurate, more scalable (can handle problems with thousands of examples), or both more accurate and more scalable than existing learning methods for MLNs. / text

As the requirements for software systems increase, their size, complexity and functionality consequently increases as well. This has a direct impact on the complexity of numerous artifacts related to the system such as specification, design, implementation and, testing models. Furthermore, as the software market becomes more and more competitive, the need for software products that are of high quality and require the least monetary, time and human resources for their development and maintenance becomes evident. Therefore, it is important that project managers and software engineers are given the necessary tools to obtain a more holistic and accurate perspective of the status of their projects in order to early identify potential risks, flaws, and quality issues that may arise during each stage of the software project life cycle. In this respect, practitioners and academics alike have recognized the significance of investigating new methods for supporting software management operations with respect to large software projects. The main target of this M.A.Sc. thesis is the design of a framework in terms of, first, a reference architecture for mining and analyzing of software project data repositories according to specific objectives and analytic knowledge, second, the techniques to model such analytic knowledge and, third, a reasoning methodology for verifying or denying hypotheses related to analysis objectives. Such a framework could assist project managers, team leaders and development teams towards more accurate prediction of project traits such as quality analysis, risk assessment, cost estimation and progress evaluation. More specifically, the framework utilizes goal models to specify analysis objectives as well as, possible ways by which these objectives can be achieved. Examples of such analysis objectives for a project could be to yield, high code quality, achieve low production cost or, cope with tight delivery deadlines. Such goal models are consequently transformed into collections of Markov Logic Network rules which are then applied to the repository data in order to verify or deny with a degree of probability, whether the particular project objectives can be met as the project evolves. The proposed framework has been applied, as a proof of concept, on a repository pertaining to three industrial projects with more that one hundred development tasks.

Un réseau logique de Markov est formé de clauses en logique du premier ordre auxquelles sont associés des poids. Cette thèse propose plusieurs méthodes pour l’apprentissage de la structure de réseaux logiques de Markov (MLN) à partir de données relationnelles. Ces méthodes sont de deux types, un premier groupe reposant sur les techniques de propositionnalisation et un second groupe reposant sur la notion de Graphe des Prédicats. L’idée sous-jacente aux méthodes à base de propositionnalisation consiste à construire un jeu de clauses candidates à partir de jeux de littéraux dépendants. Pour trouver de tels jeux, nous utilisons une méthode de propositionnalisation afin de reporter les informations relationnelles dans des tableaux booléens, qui serviront comme tables de contingence pour des test de dépendance. Nous avons proposé deux méthodes de propositionnalisation, pour lesquelles trois algorithmes ont été développés, qui couvrent les problèmes d’appprentissage génératif et discriminant. Nous avons ensuite défini le concept de Graphe des Prédicats qui synthétise les relations binaires entre les prédicats d’un domaine. Des clauses candidates peuvent être rapidement et facilement produites en suivant des chemins dans le graphe puis en les variabilisant. Nous avons développé deux algorithmes reposant sur les Graphes des Prédicats, qui couvrent les problèmes d’appprentissage génératif et discriminant. / A Markov Logic Network is composed of a set of weighted first-order logic formulas. In this dissertation we propose several methods to learn a MLN structure from a relational dataset. These methods are of two kinds: methods based on propositionalization and methods based on Graph of Predicates. The methods based on propositionalization are based on the idea of building a set of candidate clauses from sets of dependent variable literals. In order to find such sets of dependent variable literals, we use a propositionalization technique to transform relational information in the dataset into boolean tables, that are then provided as contingency tables for tests of dependence. Two propositionalization methods are proposed, from which three learners have been developed, that handle both generative and discriminative learning. We then introduce the concept of Graph of Predicates, which synthethises the binary relations between the predicates of a domain. Candidate clauses can be quickly and easily generated by simply finding paths in the graph and then variabilizing them. Based on this Graph, two learners have been developed, that handle both generative and discriminative learning.

Sistemas de computação têm se tornado maiores e mais complexos com o objetivo de lidar com a vasta quantidade de dados disponíveis. Uma tarefa decisiva em tais sistemas é classificar estes dados, bem como extrair informação útil destes. Nesta dissertação, testam-se as redes lógicas de Markov como linguagem para especificação e aprendizado de classificadores automáticos de dados. Esta linguagem combina fragmentos da lógica de primeira ordem e modelos probabilísticos gráficos (redes de Markov) em uma única representação. A junção destas duas técnicas permite a modelagem de conhecimento relacional através da lógica, e também de incertezas por meio de probabilidades e grafos não-direcionados. Neste trabalho, classificadores são aprendidos segundo dois paradigmas de aprendizado de máquina: o supervisionado, foco desta dissertação, e também o aprendizado semi-supervisionado com restrições determinísticas. Para investigar a utilidade das redes lógicas de Markov no treinamento de classificadores, uma série de experimentos de aprendizado é desenvolvida a partir de bases de dados de treino reais disponíveis em repositórios na internet. Como ferramenta auxiliar nos experimentos, esta dissertação testa também o pacote Alchemy, que provê um conjunto de algoritmos para tarefas gerais de aprendizado de máquina e inferência probabilística em redes lógicas de Markov. Para mensurar o desempenho dos classificadores aprendidos, três métricas tradicionais são empregadas: acurácia, precisão e revocação. Os resultados alcançados com classificadores semi-supervisionados com restrições indicam que a linguagem ainda não é própria para este paradigma de aprendizado. Por outro lado, o êxito obtido no desempenho dos classificadores treinados de forma supervisionada sugere que as redes lógicas de Markov são um formalismo lógico-probabilístico promissor para aplicações de classificação, e devem ser objeto de pesquisas futuras. / Computing systems have become larger and more complex in order to deal with the vast amount of available data. An important task in such systems is to classify these data, so as to extract useful information from them. In this dissertation, Markov logic networks are tested as a language to specify and learn automatic data classifiers. This language combines fragments of first-order logic and probabilistic graphical models (Markov networks), in a single representation. Together, both techniques allow one to model relational knowledge through a logic formalism, and uncertainty through probabilities and undirected graphs. In this work, data classifiers are learned by two machine learning paradigms: the supervised, the main focus of this dissertation, and also the semisupervised learning under deterministic constraints. To investigate the usefulness of Markov logic networks in training data classifiers, a set of experiments is developed from real databases available in repositories at the internet. As a support tool for experiments, this dissertation tests also the Alchemy package, which provides a set of algorithms for general machine learning tasks and probabilistic inference in Markov logic networks. To measure the performance of data classifiers, three traditional metrics are employed: accuracy, precision and recall. The results reached with semisupervised data classifiers indicate that the language is not yet suitable for learning based on this paradigm. On the other hand, the success achieved with classifiers trained in a supervised context suggests that Markov logic networks are a promising logical-probabilistic formalism to approach classification applications, and should be considered in future research.

Sistemas de computação têm se tornado maiores e mais complexos com o objetivo de lidar com a vasta quantidade de dados disponíveis. Uma tarefa decisiva em tais sistemas é classificar estes dados, bem como extrair informação útil destes. Nesta dissertação, testam-se as redes lógicas de Markov como linguagem para especificação e aprendizado de classificadores automáticos de dados. Esta linguagem combina fragmentos da lógica de primeira ordem e modelos probabilísticos gráficos (redes de Markov) em uma única representação. A junção destas duas técnicas permite a modelagem de conhecimento relacional através da lógica, e também de incertezas por meio de probabilidades e grafos não-direcionados. Neste trabalho, classificadores são aprendidos segundo dois paradigmas de aprendizado de máquina: o supervisionado, foco desta dissertação, e também o aprendizado semi-supervisionado com restrições determinísticas. Para investigar a utilidade das redes lógicas de Markov no treinamento de classificadores, uma série de experimentos de aprendizado é desenvolvida a partir de bases de dados de treino reais disponíveis em repositórios na internet. Como ferramenta auxiliar nos experimentos, esta dissertação testa também o pacote Alchemy, que provê um conjunto de algoritmos para tarefas gerais de aprendizado de máquina e inferência probabilística em redes lógicas de Markov. Para mensurar o desempenho dos classificadores aprendidos, três métricas tradicionais são empregadas: acurácia, precisão e revocação. Os resultados alcançados com classificadores semi-supervisionados com restrições indicam que a linguagem ainda não é própria para este paradigma de aprendizado. Por outro lado, o êxito obtido no desempenho dos classificadores treinados de forma supervisionada sugere que as redes lógicas de Markov são um formalismo lógico-probabilístico promissor para aplicações de classificação, e devem ser objeto de pesquisas futuras. / Computing systems have become larger and more complex in order to deal with the vast amount of available data. An important task in such systems is to classify these data, so as to extract useful information from them. In this dissertation, Markov logic networks are tested as a language to specify and learn automatic data classifiers. This language combines fragments of first-order logic and probabilistic graphical models (Markov networks), in a single representation. Together, both techniques allow one to model relational knowledge through a logic formalism, and uncertainty through probabilities and undirected graphs. In this work, data classifiers are learned by two machine learning paradigms: the supervised, the main focus of this dissertation, and also the semisupervised learning under deterministic constraints. To investigate the usefulness of Markov logic networks in training data classifiers, a set of experiments is developed from real databases available in repositories at the internet. As a support tool for experiments, this dissertation tests also the Alchemy package, which provides a set of algorithms for general machine learning tasks and probabilistic inference in Markov logic networks. To measure the performance of data classifiers, three traditional metrics are employed: accuracy, precision and recall. The results reached with semisupervised data classifiers indicate that the language is not yet suitable for learning based on this paradigm. On the other hand, the success achieved with classifiers trained in a supervised context suggests that Markov logic networks are a promising logical-probabilistic formalism to approach classification applications, and should be considered in future research.

Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.

Traditionally, machine learning algorithms assume that training data is provided as a set of independent instances, each of which can be described as a feature vector. In contrast, many domains of interest are inherently multi-relational, consisting of entities connected by a rich set of relations. For example, the participants in a social network are linked by friendships, collaborations, and shared interests. Likewise, the users of a search engine are related by searches for similar items and clicks to shared sites. The ability to model and reason about such relations is essential not only because better predictive accuracy is achieved by exploiting this additional information, but also because frequently the goal is to predict whether a set of entities are related in a particular way. This thesis falls within the area of Statistical Relational Learning (SRL), which combines ideas from two traditions within artificial intelligence, first-order logic and probabilistic graphical models to address the challenge of learning from multi-relational data. We build on one particular SRL model, Markov logic networks (MLNs), which consist of a set of weighted first-order-logic formulae and provide a principled way of defining a probability distribution over possible worlds. We develop algorithms for learning of MLN structure both from scratch and by transferring a previously learned model, as well as an application of MLNs to the problem of Web query disambiguation. The ideas we present are unified by two main themes: the need to deal with limited training data and the use of bottom-up learning techniques. Structure learning, the task of automatically acquiring a set of dependencies among the relations in the domain, is a central problem in SRL. We introduce BUSL, an algorithm for learning MLN structure from scratch that proceeds in a more bottom-up fashion, breaking away from the tradition of top-down learning typical in SRL. Our approach first constructs a novel data structure called a Markov network template that is used to restrict the search space for clauses. Our experiments in three relational domains demonstrate that BUSL dramatically reduces the search space for clauses and attains a significantly higher accuracy than a structure learner that follows a top-down approach. Accurate and efficient structure learning can also be achieved by transferring a model obtained in a source domain related to the current target domain of interest. We view transfer as a revision task and present an algorithm that diagnoses a source MLN to determine which of its parts transfer directly to the target domain and which need to be updated. This analysis focuses the search for revisions on the incorrect portions of the source structure, thus speeding up learning. Transfer learning is particularly important when target-domain data is limited, such as when data on only a few individuals is available from domains with hundreds of entities connected by a variety of relations. We also address this challenging case and develop a general transfer learning approach that makes effective use of such limited target data in several social network domains. Finally, we develop an application of MLNs to the problem of Web query disambiguation in a more privacy-aware setting where the only information available about a user is that captured in a short search session of 5-6 previous queries on average. This setting contrasts with previous work that typically assumes the availability of long user-specific search histories. To compensate for the scarcity of user-specific information, our approach exploits the relations between users, search terms, and URLs. We demonstrate the effectiveness of our approach in the presence of noise and show that it outperforms several natural baselines on a large data set collected from the MSN search engine. / text

Made available in DSpace on 2016-08-17T14:53:05Z (GMT). No. of bitstreams: 1 Lucas Rego Drumond.pdf: 6150160 bytes, checksum: 27ad4ea0ffdf273a78782ada8f04da6b (MD5) Previous issue date: 2009-10-23 / Knowledge representation formalisms, such as ontologies, have proven to be a powerful tool for enhancing the effectiveness of natural language processing, information filtering and retrieval and so on. Besides these tasks, ontologies are also crucial for the Semantic Web, a new generation of the Web that aims at structuring its content in such a way that it can be more effectively processed by machines. However, knowledge systems suffer from the so called knowledge acquisition bottleneck, i.e. the difficulty in constructing knowledge bases. An approach for this problem is to provide automatic or semi-automatic support for ontology construction, a field of research known as ontology learning. This work discusses the state of the art of ontology learning techniques and proposes and approach for supporting the ontology construction process through the automatization of the concept hierarchy extraction from textual sources. The proposed process is composed by two techniques, namely PRECE (Probabilistic Relational Concept Extraction) and PREHE (Probabilistic Relational Hierarchy Extraction). The PRECE technique extracts ontology concepts from textual sources while the PREHE technique extracts taxonomic relationships between the concepts extracted by PRECE. Both techniques use Markov logic networks, an approach for statistical relational learning that combines first order logic with Markov networks. The PRECE and PREHE techniques were evaluated in the touristic domain and their results were compared with an ontology manually developed by a domain expert. / Os formalismos de representação do conhecimento como as ontologias têm se mostrado uma poderosa ferramenta para melhorar a efetividade de sistemas de processamento da linguagem natural, recuperação e filtragem de informação e muitas outras tarefas. Além disso, as ontologias são essenciais para a Web Semântica, uma nova geração da Web que visa estruturar o conteúdo da mesma de modo que este possa ser processado de forma mais efetiva pelas máquinas. Entretanto, os sistemas de conhecimento sofrem do problema conhecido como o gargalo da aquisição do conhecimento, que nada mais é do que a dificuldade de construção das bases de conhecimento. Uma abordagem para este problema é o suporte automático ou semi-automático à construção de ontologias. Este campo de pesquisa é conhecido como aprendizagem de ontologias. Este trabalho discute o estado da arte das técnicas de aprendizagem de ontologias e propõe uma abordagem para o suporte ao processo de construção de ontologias através da automatização da extração de hierarquias de conceitos a partir de fontes textuais. O processo proposto é composto por duas técnicas, a PRECE (Probabilistic Relational Concept Extraction) para a extração de conceitos e a PREHE (Probabilistic Relational Hierarchy Extraction) para a descoberta de relacionamentos taxonômicos entre os conceitos extraídos pela PRECE. As duas técnicas fazem uso das Redes Lógicas de Markov, uma abordagem da aprendizagem probabilística relacional que combina a lógica de primeira ordem com as redes de Markov. As técnicas PRECE e PREHE foram avaliadas no domínio turístico comparando os seus resultados com uma ontologia desenvolvida manualmente por especialistas neste domínio.