Uma métrica fuzzy para aprendizagem de estruturas de redes bayesianas pelo método de Monte Carlo e cadeias de Markov

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2014.A aprendizagem de estrutura de redes bayesianas (RB) a partir dos dados é considerada uma tarefa complexa, uma vez que o número de estruturas possíveis cresce exponencialmente de acordo com o número de variáveis. Existem dois métodos principais para esta tarefa de aprendizagem de estruturas de RB: o método de independência condicional, que busca uma estrutura consistente com os testes de independência realizados nos dados; o método de busca heurística, que explora o espaço de busca avaliando as possíveis estruturas por meio de algoritmos de busca. Além desses dois métodos, também são considerados os algoritmos híbridos, onde os dois métodos são aplicados na tarefa. A principal falha dessas abordagens tradicionais é que elas não conseguem identificar todas as relações existentes nos dados, sendo necessário investigar novas abordagem. Desta forma, esta pesquisa apresenta o desenvolvimento de uma métrica fuzzy de avaliação com um método de busca heurística para aprendizagem de estrutura de redes bayesianas, utilizando Monte Carlo via Cadeias de Markov. As diferentes métricas de avaliação de redes bayesianas utilizadas permitem identificar determinadas propriedades nas redes. Essas propriedades são determinadas em função da métrica aplicada. A combinação em uma métrica fuzzy possibilita avaliar diferentes propriedades simultaneamente. Os resultados deste trabalho foram avaliados no contexto de bases sintéticas por meio da comparação com outros algoritmos, convergência das cadeias de Markov e tempo de processamento. Os resultados evidenciam, apesar do tempo de processamento, que a métrica proposta, além de compatível com os algoritmos clássicos, melhorou o processo de avaliação de estruturas combinando diferentes métricas em uma métrica fuzzy.Abstract : Learning bayesian networks (BN) from data is considered a complex task, since the number of possible structures grows exponentially with the number of variables. There are two main approaches for learning BN: methods based on independence tests, seeking structures consistente with the tests performed on the data; methods based on heuristic search, exploring the search space with a search algorithm, evaluating the possible structures. Besides these two approaches, there are hybrid algorithms, where both methods are applied to the task. The main fault of these approaches is that they still fail to identify all existing relationships in the data, so it is necessary to investigate new approaches. This research presents the development of a fuzzy score metric in a heuristic search method for learning Bayesian network structures, in a Markov Chain Monte Carlo algorithm. Different score metrics used to learn BN structures identify certain properties in these networks. These properties are determined based on the score applied. The combination of these scores in a fuzzy metric enables the evaluation of different properties simultaneously. Results of this research were evaluated in the context of synthetic bases by comparing with other algorithms, convergence of Markov chains and processing time. The results show, despite the processing time, that the proposed metric is compatible with traditional algorithms, and improved the evaluation process of structures, combining different score metrics into a fuzzy metric

The ARK platform: enabling risk management through semantic web technologies

This paper describes the Access Risk Knowledge (ARK) platform and ontologies for socio-technical risk analysis using the Cube methodology. Linked Data is used in ARK to integrate qualitative clinical risk management data with quantitative operational data and analytics. This required the development of a novel clinical safety management taxonomy to annotate qualitative risk data and make it more amenable to automated analysis. The platform is complemented by other two ontologies that support structured data capture for the Cube sociotechnical analysis methodology developed by organisational psychologists at Trinity College Dublin. The ARK platform development and trials have shown the benefits of a Semantic Web approach to flexibly support data integration, making qualitative data machine readable and building dynamic, high-usability web applications applied to clinical risk management. The main results so far are a self-annotated, standards-based taxonomy for risk and safety management expressed in the W3C’s standard Simple Knowledge Organisation System (SKOS) and a Cube data capture, curation and analysis platform for clinical risk management domain experts. The paper describes the ontologies and their development process, our initial clinical safety management use case and lessons learned from the application of ARK to real-world use cases. This work has shown the potential for using Linked Data to integrate operational and safety data into a unified information space supporting more continuous, adaptive and predictive clinical risk management

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

Evaluating the creation and understanding of uplift mappings

The majority of data in the Web still resides in other formats than the Resource Description Framework (RDF). RDF is a W3C recommendation for representing information in the Web, facilitating data exchange, data integration and others. One of the main tasks when upgrading legacy systems to the Semantic Web is the conversion of data. The process of converting data in any format into RDF is called uplift. The key stakeholders in this process are web developers, software programmers specialized in the development of systems for the web, and ontology engineers, experts in semantic web technologies such as ontologies, RDF and so on. Several solutions have been proposed, however, these still focus on Semantic Web experts. To facilitate the uplift process and to make the technology available to a wider set of stakeholders, I have developed a method to represent uplift mappings visually. The method draws inspiration from visual programming languages such as Google?s Blockly. Blockly has been used in many projects, such as code.org?s introduction courses. In the visual representation, blocks represent a mapping that automatically generates an uplift mapping. In this experiment, I aim to investigate if such a visual representation: (i) facilitates the creation of accurate uplift mappings; (ii) eases the understandability of uplift mappings; and (iii) imposes an optimal mental workload on users

Juma Uplift: Using a Block Metaphor for Representing Uplift Mappings

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Using a Block Metaphor for Representing R2RML Mappings

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Incorporating Functions in Mappings to Facilitate the Uplift of CSV Files into RDF

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An Editor that Uses a Block Metaphor for Representing Semantic Mappings in Linked Data

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