In order to compete with the global giants, enterprises are concentrating on their core competencies and collaborating with organizations that compliment their skills and core activities. The current trend is to develop temporary alliances of independent enterprises, in which companies can come together to share skills, core competencies and resources. However, knowledge sharing and communication among multidiscipline companies is a complex and challenging problem. In a collaborative environment, the meaning of knowledge is drastically affected by the context in which it is viewed and interpreted; thus necessitating the treatment of structure as well as semantics of the data stored in enterprise repositories. Keeping the present market and technological scenario in mind, this research aims to propose tools and techniques that can enable companies to assimilate distributed information resources and achieve their business goals.

This thesis deals with the use of knowledge technologies in support of self-organization of people with joint political goals. It first provides a theoretical background for a development of a social-semantic system intended to support self-organization and then it applies this background in the development of a core ontology and algorithms for support of self-organization of people. It also presents a design and implementation of a proof-of-concept social-semantic web application that has been built to test our research. The application stores all data in an RDF store and represents them using the core ontology. Descriptions of content are disambiguated using the WordNet thesaurus. Emerging politically engaged groups can establish themselves into local political initiatives, NGOs, or even new political parties. Therefore, the system may help people easily participate on solutions of issues which are influencing them.

The DBpedia project aims at extracting information based on semi-structured data present in Wikipedia articles, interlinking it with other knowledge bases, and publishing this information as RDF freely on the Web. So far, the DBpedia project has succeeded in creating one of the largest knowledge bases on the Data Web, which is used in many applications and research prototypes. However, the manual effort required to produce and publish a new version of the dataset – which was already partially outdated the moment it was released – has been a drawback. Additionally, the maintenance of the DBpedia Ontology, an ontology serving as a structural backbone for the extracted data, made the release cycles even more heavyweight. In the course of this thesis, we make two contributions: Firstly, we develop a wiki-based solution for maintaining the DBpedia Ontology. By allowing anyone to edit, we aim to distribute the maintenance work among the DBpedia community. Secondly, we extend DBpedia with a Live Extraction Framework, which is capable of extracting RDF data from articles that have recently been edited on the English Wikipedia. By making this RDF data automatically public in near realtime, namely via SPARQL and Linked Data, we overcome many of the drawbacks of the former release cycles.

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 Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-04-06T12:24:58Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Kalil Araujo Bispo - Versão Final da Tese para a Biblioteca.pdf: 2225128 bytes, checksum: 8010e027c8884242fe1de82687aef45f (MD5) / Made available in DSpace on 2016-04-06T12:24:58Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Kalil Araujo Bispo - Versão Final da Tese para a Biblioteca.pdf: 2225128 bytes, checksum: 8010e027c8884242fe1de82687aef45f (MD5) Previous issue date: 2015-08-24 / CAPEs / As Redes de Sensores Sem Fio (RSSFs) são redes com recursos limitados, como processamento, largura de banda, memória e, o mais importante, energia. Dessa forma, as aplicações para nós sensores devem criar condições de realizar suas operações de sensoriamento e processamento no maior tempo possível, como também mecanismos que possam ajudar na economia de energia, por exemplo, a utilização de melhores algoritmos, agregação de dados, mecanismos de auto-gerenciamento, dentre outros, respeitando as limitações de recursos das RSSF. Algumas pesquisas na área mostram que solucionar esse tipo de problema não é uma tarefa fácil de ser resolvida. Sendo assim, este trabalho propõe uma solução de economia de energia para RSSF e uma forma comum de compartilhamento de dados entre aplicações e redes diferentes, baseada em uma Infraestrutura Semântica para RSSF chamada SITRUS. Ela utiliza reconfiguração paramétrica nos nós sensores em tempo de execução, a partir de dados de sensoriamento processados fora da RSSF, utilizando ontologias desenvolvidas com esse propósito para o processamento desses dados. SITRUS é formada por duas partes importantes: o Middleware Ciente de Reconfiguração para Rede de Sensores Sem Fio (RAMSES), responsável pelo transporte de dados e gerenciamento de serviços das aplicações que são executadas nos nós sensores; e o Módulo de Processamento Semântico da Informação (SIP), que tem por finalidade categorizar os dados para a geração da base de dados semântica. Esta base de dados servirá para a tomada de decisões de reconfiguração dos nós sensores e para o processamento de consultas sobre as RSSFs. A escolha por esse modelo se deve ao fato de que o processamento referente à reconfiguração da RSSF não sofre intervenção humana. O processamento é determinado pelo SIP e executado pelo RAMSES. Dessa forma, segue-se um modelo baseado em semântica formal. Pretende-se também que a SITRUS favoreça a integração de diferentes aplicações pelo compartilhamento de dados relativos a um mesmo contexto. Os benefícios desta abordagem incluem o enriquecimento dos dados pela associação de seu significado, e não apenas pela sintaxe dos dados, facilitando assim o seu acesso e eliminando ambiguidades. Como forma de demonstrar a eficiência da SITRUS, uma avaliação experimental do consumo de energia com algumas aplicações e diferentes cenários foi realizada, mostrando em seus resultados que a SITRUS atende ao que foi proposto no que diz respeito ao gerenciamento do consumo de energia. / Wireless Sensor Networks (WSNs) are networks with limited resources such as processing, bandwidth, memory, and most importantly, energy. Thus, applications for sensor nodes must create conditions to perform their sensing and processing operations as long as possible, as well as mechanisms that can help to save energy, for example, such as the use of better algorithms, data aggregation, self-management mechanisms, among others, in compliance the resource limitations of WSN. Some research in the area show that solving this problem is not an easy task to be addressed. Thus, this paper proposes a power saving solution for WSN and a common way of sharing data between different applications and networks based on a Semantic Infrastructure for WSN called SITRUS. It uses parametric reconfiguration in sensor nodes at runtime, from sensing data processed outside the WSN using ontologies developed for this purpose for processing such data. SITRUS consists of two major parts: RAMSES, responsible for data transport and service management applications that run on the sensor nodes; and SIP, which is intended to categorize the data for generating the semantic database. This database will serve for decision making on the reconfiguration of sensor nodes and for query processing on WSNs. The choice for this model is due to the fact that the processing related to the reconfiguration of WSN does not suffer human intervention. Processing is determined by SIP and performed by RAMSES. Thus, it follows a model based on formal semantics. It is intended that the SITRUS encourages the integration of different applications by sharing data on a same context. The benefits of this approach include the enrichment of the data by the association of its meaning, and not only by the syntax of the data, thus facilitating their access and eliminating ambiguities. In order to demonstrate the effectiveness of SITRUS, an experimental evaluation of power consumption with some applications and different scenarios was performed, and the results shows that SITRUS serves to what has been proposed in regard to management of the energy consumption.