Quem@PUC is an Information Retrieval System available on the Web that allows searching for researchers and professors based on a keyword list of research related terms. It publicizes research and teaching activities from the PUC-Rio community to society in general. The idea is to integrate information from professors from administrative systems, courses offered, and researchers’ Lattes CVs. Data sources are converted to RDF format using domain ontologies, then stored in a NoSQL database that supports native free-text indexing on triple objects. Search results include names, academic papers, teaching activities, and contact links.
This work presents the design and implementation of two web-based search systems, Busc@NIMA and Quem@PUC. Both systems allow the identification of research and development projects, besides existing competencies in laboratories and departments involving professors and researchers at PUC-Rio University. Our applications are based on a list of search-related terms that are matched to the dataset composed of PUC-Rio’s Lattes CVs offered courses, information from administrative systems, and specific keywords that are input by the professors/researchers themselves. To integrate all the needed data, we consider multiple database and search technologies, such as XML, RDF, TripleStores, and Relational Databases. Search results include professor’s name, academic papers, teaching activities, contact links, keywords, and laboratories of those involved with the subject represented by the set of keywords input. We describe the main features that show how our systems work.
Este trabalho apresenta o projeto e a construção de Sistemas de Recuperação de Informações que permitem a identificação de projetos de pesquisa e/ou desenvolvimento, e as competências existentes em laboratórios e departamentos, coordenados por integrantes do quadro de professores-pesquisadores da PUC-Rio, a partir da busca por uma ou uma lista de palavras-chave. As fontes de informação que compõem o banco de dados do projeto são convertidas para o formato RDF usando ontologias de domínio, e são armazenadas em uma base NoSQL que suporta indexação de texto livre nativamente. Os resultados da busca incluem nomes, produções científicas diversas, atividades de ensino e links para contato. Ilustramos nossa solução com dois sistemas em desenvolvimento: Busc@NIMA e Quem@PUC.
This paper discusses conceptual and logical data models for social media datasets and applications. On the one hand, we focus on the data representation requirements from the available APIs of some social network systems. On the other hand, we consider those application requirements for information manipulation. We propose a conceptual meta-model and one possible instantiation. We also give preliminary practical results considering relational and graph database systems.
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