In this work, we describe two of our experiences applying BDD: one at a large publisher of financial information and business news, and the other in a secure collaboration platform vendor. The team composition involves both a local team and an external service provider. We analyze two adoption processes, one driven by the product team, and the other driven by the development team. We cover the communication benefits and impacts on areas such as stakeholder alignment, software design, code refactoring, and bug fixing. We also discuss lessons learned, caveats and how we plan to expand this practice in the future.

The Semantic Web community has successfully contributed to a remarkable number of RDF datasets published on the Web. However, to use and build applications on top of Linked Data is still a cumbersome and time-demanding task. We present KBox, an open-source platform that facilitates the distribution and consumption of RDF data. We show the different APIs implemented by KBox, as well as the processing steps from a SPARQL query to its corresponding result. Additionally, we demonstrate how KBox can be used to share RDF knowledge graphs and to instantiate SPARQL endpoints.

Named Entity Recognition and Disambiguation (NERD) systems have recently been widely researched to deal with the significant growth of the Web. NERD systems are crucial for several Natural Language Processing (NLP) tasks such as summarization, understanding, and machine translation. However, there is no standard interface specification, i.e. these systems may vary significantly either for exporting their outputs or for processing the inputs. Thus, when a given company desires to implement more than one NERD system, the process is quite exhaustive and prone to failure. In addition, industrial solutions demand critical requirements, e.g., large-scale processing, completeness, versatility, and licenses. Commonly, these requirements impose a limitation, making good NERD models to be ignored by companies. This paper presents TANKER, a distributed architecture which aims to overcome scalability, reliability and failure tolerance limitations related to industrial needs by combining NERD systems. To this end, TANKER relies on a micro-services oriented architecture, which enables agile development and delivery of complex enterprise applications. In addition, TANKER provides a standardized API which makes possible to combine several NERD systems at once.

Generation Z are the next generation of college students. These students have demanded new teaching practices. The use of visual resources for learning and the anxiety for immediate results are one of their demands. This paper discusses a strategy for teaching and learning data structures supported by the DSGraph library. This strategy has been used in Federal University of Juiz de Fora in order to adapt the teaching of abstract concepts such data structures and its algorithms for the new generation of students. The library aims, through visual representation, the elucidation of the concepts covered during the course of data structures, combined with active student participation in the construction and development of its solutions. An experimental study was carried out for two years with two groups of students. The first group was submitted to the proposed methodology and other group was submitted to the traditional methodology. The results show that the methodology proposed reaches satisfactory improvement of the approval rate and the students’ satisfaction.

In the last couple of years the amount of structured open government data has increased significantly. Already now, citizens are able to leverage the advantages of open data through increased transparency and better opportunities to take part in governmental decision making processes. Our approach increases the interoperability of existing but distributed open governmental datasets by converting them to the RDF-based NLP Interchange Format (NIF). Furthermore, we integrate the converted data into a geodata store and present a user interface for querying this data via a keyword-based search. The language resource generated in this project is publicly available for download and also via a dedicated SPARQL endpoint.

Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. We address this issue by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text.

Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text. We evaluate our approach on eight different datasets against state-of-the-art named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.

Este trabalho propoe a criacao de uma biblioteca grafica capaz de exibir representacoes graficas das estruturas basicas usadas na disciplina de Estrutura de Dados (fila, pilha, arvore, lista encadeada, grafo, etc) implementadas pelos alunos em exercicios e trabalhos das disciplinas de Algoritmos, Estrutura de Dados, Laboratorio de Programacao I e Estruturas de Dados II. A exibicao grafica dessas estruturas poderao ser utilizadas para verificar atualizacoes dessas estruturas na implementacao de metodos proprios como insercao, remocao e busca de um determinado no, ajudando o academico na percepcao do funcionamento e elucidando questoes em aplicacoes praticas nos problemas computacionais quanto a desempenho, custo de armazenamento e otimizacoes.