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Ontology Matching is one of the hottest topic within the Semantic Web of recent years. There is still ample of space for improvement in terms of performance. Furthermore, current ontology matchers mostly concentrate on simple entity to entity matching. However, matching of whole structures could bring some additional complex relationships. These structures of ontologies can be captured as ontology patterns. The main theme of this thesis is an examination of pattern-based ontology matching enhanced with ontology transformation and pattern-based ontology alignment evaluation. The former is examined due to its potential benefits regarding complex matching and matching as such. The latter is examined because complex hypotheses could be beneficial feedback as complement to traditional evaluation methods. These two tasks are related to four different topics: ontology patterns, ontology transformation, ontology alignment evaluation and ontology matching. With regard to those four topics, this work covers the following aspects: * Examination of different aspects of ontology patterns. Particularly, description of relevant ontology patterns for ontology transformation and for ontology matching (such as naming, matching and transformation patterns). * Description of a pattern-based method for ontology transformation. * Introduction of new methods for an alignment evaluation; including using patterns as a complex structures for more detailed analysis. * Experiments and demonstrations of new concepts introduced in this thesis. The thesis first introduces naming pattern and matching pattern classification on which ontology transformation framework is based. Naming patterns are useful for detection of ontology patterns and for generation of new names for entities. Matching patterns are basis for transformation patterns in terms of sharing some building blocks. In comparison with matching patterns, transformation patterns have transformation links that represent way how parts of ontology patterns are transformed. Besides several evaluations and implementations, the thesis provides a demonstration of getting complex matching due to ontology transformation process. Ontology transformation framework has been implemented in Java environment where all generic patterns are represented as corresponding Java objects. Three main implemented services are made generally available as RESTful services: ontology pattern detection, transformation instruction generation and ontology transformation.

Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-30T17:33:51Z No. of bitstreams: 1 TES_BERNARDO_SEVERO_DE_SOUZA_COMPLETO.pdf: 5113763 bytes, checksum: a24628d427a0a60b3a6ea0c5200d5dfd (MD5) / Made available in DSpace on 2017-06-30T17:33:51Z (GMT). No. of bitstreams: 1 TES_BERNARDO_SEVERO_DE_SOUZA_COMPLETO.pdf: 5113763 bytes, checksum: a24628d427a0a60b3a6ea0c5200d5dfd (MD5) Previous issue date: 2017-02-20 / O aumento do volume de dados n?o estruturados na Web nas ?ltimas d?cadas tem sido impulsionado pelo surgimento de novos meios de comunica??o, dispositivos e tecnologias. Neste contexto se desenvolve a Web Sem?ntica, cujo objetivo ? o de atribuir uma camada de representa??o de conhecimento a esses dados, facilitando o tratamento por processos automatizados. Ontologias s?o elementos chave da Web Sem?ntica, oferecendo uma descri??o dos conceitos e dos relacionamentos entre os mesmos para um dom?nio espec?fico. Entretanto, ontologias de um mesmo dom?nio podem divergir em sua estrutura, granularidade ou terminologia, necessitando que um processo de mapeamento entre as mesmas seja realizado, produzindo um conjunto de correspond?ncias entre entidades semanticamente relacionadas (alinhamento). Um n?mero crescente de abordagens de mapeamento tem surgido na literatura e a necessidade de avaliar e comparar qualitativamente os alinhamentos produzidos se faz presente. Tarefas que fazem uso de alinhamentos passaram a demandar melhores representa??es gr?ficas dos mesmos. Neste contexto, foi realizada uma pesquisa com especialistas em alinhamentos para identificar os aspectos mais importantes em uma visualiza??o de alinhamentos. Este trabalho apresenta ent?o uma abordagem adaptativa de visualiza??o para alinhamentos, que permite ao usu?rio escolher como e o que visualizar, de acordo com prefer?ncias pr?prias ou para uma atividade sendo realizada no momento (cria??o, manipula??o, avalia??o, etc.). Por fim, um prot?tipo foi constru?do com o intuito de validar a solu??o. Os resultados obtidos da avalia??o dos usu?rios com o prot?tipo mostram que a abordagem lida com os problemas que se prop?e a resolver, com uma margem para trabalhos futuros em formas de visualiza??o de alinhamentos. / The increase in the volume of unstructured web data in recent decades has been driven by the arising of new media, devices and technologies. In this context, the Semantic Web was developed, whose objective is to provide a layer of knowledge representation to that data, facilitating the treatment by automated processes. Ontologies are key elements of the Semantic Web, providing a description of the concepts and relationships between them, for a specific domain. However, ontologies of the same domain may differ in structure, granularity or terminology, requiring a process of matching between them to be performed, producing a set of correspondences between semantically related entities (alignment). A growing number of matching approaches have emerged in the literature, and the need to evaluate and qualitatively compare the produced alignments is presented. Tasks that make use of alignments started to demand better graphical representations for it. In this context, a survey was conducted with alignment specialists to identify the most important aspects in an alignment visualization. This work presents an adaptative approach for alignment visualization, that allows users to choose how and what to visualize, according to their own preferences or the task being performed at that moment (creation, manipulation, evaluation, etc.). Finally, a prototype was built with the purpose of validating the solution. The results obtained from the prototype validation with users show that the approach handles the problems it proposes to solve, with a margin for future work on alignment visualization.

Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches. In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model. The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.

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[pt] Sistemas de informação, desenvolvidos para diversos setores econômicos, necessitam com maior freqüência capacidade de rastreabilidade dos dados. Para habilitar tal capacidade, é necessário modelar a proveniência dos dados. Proveniência permite testar conformidade com a legislação, repetição de experimentos, controle de qualidade, entre outros. Habilita também a identificação de agentes (pessoas, organizações ou agentes de software) e pode ser utilizada para estabelecer níveis de confiança para as transformações dos dados. Esta dissertação propõe um modelo genérico de proveniência criado com base no alinhamento de recortes de ontologias de alto nível, padrões internacionais e propostas de padrões que tratam direta ou indiretamente de conceitos relacionados à proveniência. As contribuições da dissertação são portanto em duas direções: um modelo conceitual para proveniência - bem fundamentado - e a aplicação da estratégia de projeto conceitual baseada em alinhamento de ontologias. / [en] Information systems, developed for several economic segments, increasingly demand data traceability functionality. To endow information systems with such capacity, we depend on data provenance modeling. Provenance enables legal compliance, experiment validation, and quality control, among others . Provenance also helps identifying participants (determinants or immanents) like people, organizations, software agents among others, as well as their association with activities, events or processes. It can also be used to establish levels of trust for data transformations. This dissertation proposes a generic conceptual model for provenance, designed by aligning fragments of upper ontologies, international standards and broadly recognized projects. The contributions are in two directions: a provenance conceptual model - extensively documented - that facilitates interoperability and the application of a design methodology based on ontology alignment.