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Both process and outcome indicators point to the effectiveness of efforts to reduce perinatal transmission in São Paulo State.

O entendimento da rede de colaboração de um departamento de Pós-graduação permite a identificação de uma série de itens relacionados ao processo de colaboração científica, tais como: nível de colaboração da comunidade envolvida, distância média entre os colaboradores, autores que mais colaboram, grupos que colaboram com mais intensidade, etc. Através da produção bibliográfica informada ao sistema Coleta de Dados da CAPES, referente ao período de 2005-2012, redes de coautoria foram criadas e analisadas através de métricas de análise de rede social. Foi encontrado um índice médio de colaboração de 3,4 autores por publicação, redes de coautoria com baixa densidade, um maior número de componentes nos primeiros anos do programa e autores com alta produtividade, porém, com baixa centralidade de grau. Acredita-se que a análise e discussão dos resultados apresentados nesse artigo alcançaram o objetivo macro de diagnosticar a produção bibliográfica do programa, em termos de colaboração científica, e espera-se que seja uma ferramenta útil para tomada de decisão em nível organizacional e individual.

Abstract. In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

This paper proposes a process based on learning analytics and recommender systems targeted at making suggestions to students about their remote laboratories activities and providing insights to all stakeholders taking part in the learning process. To apply the process, a log with requests and responses of remote experiments from the VISIR project were analyzed. A request is the setup of the experiment including the assembled circuits and the configurations of the measuring equipment. In turn, a response is a message provided by the measurement server indicating measures or an error when it is not possible to execute the experiment. Along the two phases of analysis, the log was analyzed and summarized in order to provide insights about students' experiments. In addition, there is a recommendation service responsible for analyzing the requests thus returning, in case of error, precise information about the assembly of circuits or configurations. The evaluation of the process is consistent in what regards its ability to afford recommendations to the students as they carry out the experiments. Moreover, the summarized information intends to offer teachers means to better understand and develop strategies to scaffold students' learning.

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BackgroundWeb-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive.MethodWe focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology.ResultsWe present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances.ConclusionWe have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.

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