Génération automatique d'alignements complexes d'ontologies

Le web de données liées (LOD) est composé de nombreux entrepôts de données. Ces données sont décrites par différents vocabulaires (ou ontologies). Chaque ontologie a une terminologie et une modélisation propre ce qui les rend hétérogènes. Pour lier et rendre les données du web de données liées interopérables, les alignements d'ontologies établissent des correspondances entre les entités desdites ontologies. Il existe de nombreux systèmes d'alignement qui génèrent des correspondances simples, i.e., ils lient une entité à une autre entité. Toutefois, pour surmonter l'hétérogénéité des ontologies, des correspondances plus expressives sont parfois nécessaires. Trouver ce genre de correspondances est un travail fastidieux qu'il convient d'automatiser. Dans le cadre de cette thèse, une approche d'alignement complexe basée sur des besoins utilisateurs et des instances communes est proposée. Le domaine des alignements complexes est relativement récent et peu de travaux adressent la problématique de leur évaluation. Pour pallier ce manque, un système d'évaluation automatique basé sur de la comparaison d'instances est proposé. Ce système est complété par un jeu de données artificiel sur le domaine des conférences.The Linked Open Data (LOD) cloud is composed of data repositories. The data in the repositories are described by vocabularies also called ontologies. Each ontology has its own terminology and model. This leads to heterogeneity between them. To make the ontologies and the data they describe interoperable, ontology alignments establish correspondences, or links between their entities. There are many ontology matching systems which generate simple alignments, i.e., they link an entity to another. However, to overcome the ontology heterogeneity, more expressive correspondences are sometimes needed. Finding this kind of correspondence is a fastidious task that can be automated. In this thesis, an automatic complex matching approach based on a user's knowledge needs and common instances is proposed. The complex alignment field is still growing and little work address the evaluation of such alignments. To palliate this lack, we propose an automatic complex alignment evaluation system. This system is based on instances. A famous alignment evaluation dataset has been extended for this evaluation

Geração semi-automática de mapeamentos de vocabulários entre datasets da web de dados usando SPARQL

Atualmente, a web tem evoluído de um espaço global de documentos interligados ( Web de Documentos) para um espaço global de dados vinculados ( Web de Dados), de modo a que tanto os humanos como os agentes computacionais consigam compreender e extrair informações úteis desses dados. No entanto, para que seja possível possuir um dia uma Web de Dados, é necessário, em primeiro lugar, dar semântica aos dados. Neste sentido, emergiu uma nova abordagem, designada por Web Semântica, cujo principal objetivo é facilitar a interpretação e integração de dados na web. Na Web Semântica, utilizamos habitualmente as ontologias para descrever formalmente a semântica dos dados. No entanto, à medida que o número de ontologias vai aumentado, é bastante comum existir heterogeneidade entre elas, já que cada ontologia pode usar vocabulários diferentes para representar dados acerca de uma mesma área de conhecimento. Esta heterogeneidade impossibilita a recuperação de informações por parte dos agentes computacionais sem que haja intervenção humana. Para fazer face aos problemas relacionados com a heterogeneidade, é muito comum efetuar-se mapeamentos entre as ontologias. Existem diversas linguagens no mercado que permitem traduzir e mapear ontologias, dentro as quais destacamos a linguagem SPARQL Protocol and RDF Query Language (SPARQL 1.1) 1 e R2R 2 . Neste trabalho decidimos usar o SPARQL 1.1 como linguagem de mapeamento entre ontologias, pois é um padrão recomendado pelo World Wide Web Consortium (W3C) e amplamente utilizado pela comunidade. Como esta linguagem é complexa e necessita que o utilizador tenha experiência na definição e criação de mapeamentos, propomos uma ferramenta, chamada SPARQL Mapping with Assertions (SMA), que visa auxiliar os utilizadores no processo de geração de mapeamentos SPARQL 1.1 entre ontologias. A ferramenta SMA é constituída por quatro partes: (1) configuração inicial das ontologias: o utilizador indica quais as ontologias que deseja mapear, assim como a linguagem em que os ficheiros das mesmas estão escritos; (2) criação das Assertivas de Mapeamento (AMs): através da interface gráfica, o utilizador especifica quais os mapeamentos que deseja definir, incluindo possíveis transformações ou filtros que sejam necessários aplicar aos dados;(3) configuração para a geração de mapeamentos: o utilizador introduz o ficheiro com o Dataset da ontologia fonte e identifica a linguagem de serialização em que o mesmo está escrito. Além disso, também escolhe qual a linguagem de serialização que deseja aquando da geração de triplos; (4) geração de triplos através dos mapeamentos SPARQL 1.1: a partir dos pontos anteriores, a nossa ferramenta irá retornar um ficheiro com todos os resultados na linguagem de serialização escolhida. A nossa ferramenta permite ainda exportar todos os mapeamentos criados, quer seja através das linguagens formais (assertivas ou regras de mapeamentos) ou dos mapeamentos SPARQL 1.1

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

A Jigsaw Puzzle Metaphor for Representing Linked Data Mappings

This thesis presents a visual representation approach for Linked Data mappings known as Jigsaw Puzzles for Representing Mappings, or Juma. The term Linked Data refers to a set of best practices for publishing and interlinking data on the Web. A Linked Data dataset is structured information encoded using the Resource Description Framework (RDF), in which resources are identified by and linked with other datasets using HTTP URIs. Linked Data datasets cover a wide range of knowledge domains, where often concepts overlap. In such cases, mappings can be created to reduce heterogeneity and facilitate the consumption of information by informing agents which concepts are related, and how. These types of mappings are called semantic mappings. Another area in which we find use for mappings is when transforming data from one representation to another ? from non-RDF to RDF for example. We call those mappings uplift mappings. Producing such mappings can be difficult, even for experts in Semantic Web technologies, requiring knowledge on the specifics of the mapping language being used as well as significant amount of human effort for their creation, modification, curation and maintenance. Nonetheless, literature suggests that this user involvement is fundamental for producing quality mappings. Suitable visual representations may be used to support user involvement and alleviate the knowledge required for producing Linked Data mappings. Through a systematic literature review, a set of requirements for a visual representation for Linked Data mappings were defined. Juma was then proposed as a novel approach, based on the block metaphor, for the representation of mappings in Linked Data. The block ? or jigsaw ? metaphor was chosen as it takes advantage of the user?s familiarity to jigsaw puzzles, fosters users to explore the combinations of blocks, and for being accessible to experts and non-experts alike. Juma leverages the use of the block metaphor in order to facilitate the interpretation of mappings in Linked Data. In Juma, blocks are used to abstract and capture different mapping constructs, where the connection of the different blocks form a mapping. Each block is then translated to an equivalent mapping representation, which can be done for distinct mapping languages. The Juma approach was evaluated through five experiments categorized in three aspects: creation (and editing), understanding, and expressiveness. The creation of mappings was evaluated through two user experiments, where participants were asked to create a mapping using applications that apply the Juma approach for the representation of mappings. Another user experiment was conducted to evaluate the understanding of mappings represented using a Juma application. Finally, two experiments were conducted to evaluate the expressiveness of the Juma approach in the representation of uplift and semantic mappings, respectively. These evaluations indicated that the Juma approach is effective in representing uplift and semantic mappings, and that it aids users in the creation, editing and understanding of Linked Data mappings. The research in this thesis has yielded one major contribution and three minor contributions. The major contribution is the design and development of the Jigsaw Puzzles for Representing Mappings (Juma) approach. The first minor contribution is the Juma R2RML application. Juma R2RML applies the Juma approach to the R2RML mapping language. The second minor contribution is the Juma Uplift application. Juma Uplift has a higher level of abstraction in order to be able to generate mappings using multiple distinct mapping languages. The Juma R2RML and Juma Uplift applications apply the Juma approach in the representation of uplift mappings. The third minor contribution is the Juma Interlink application. Juma Interlink applies the Juma approach in the representation of semantic mappings