Effective Tooling for Linked Data Publishing in Scientific Research

Purohit, Sumit; Smith, William P.; Chappell, Alan R.; West, Patrick; Lee, Benno; Stephan, Eric G.; Fox, Peter

doi:10.1109/icsc.2016.87

Cited by 7 publications

(3 citation statements)

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“…One of the key features evaluated in our framework is the support for transformation functions, as data manipulation is typically needed during the uplift process (Purohit et al, 2016). These functions can be used to capture both domain knowledge (e.g., transforming units) and other, more syntactic, data manipulation tasks (e.g., transforming values to create valid IRIs).…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of uplift mapping languages

Crotti

Debruyne

Brennan

et al. 2017

IJWIS

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Purpose: Typically tools that map non-RDF data into RDF format rely on the technology native to the source of the data when data transformation is required. Depending on the data format, data manipulation can be performed using underlying technology, such as RDBMS for relational databases or XPath for XML. For CSV/Tabular data there is no such underlying technology, and instead it requires either a transformation of source data into another format or pre/post-processing techniques. In this paper we evaluate the state of the art in CSV uplift tools. Based on this evaluation, a method that incorporates data transformations into uplift mapping languages by means of functions is proposed and evaluated. Design/methodology/approach: In order to evaluate the state of the art in CSV uplift tools we present a comparison framework and have applied it to such tools. A key feature evaluated in the comparison framework is data transformation functions. We argue that existing approaches for transformation functions are complex-in that a number of steps and tools are required. Our proposed method, FunUL, in contrast, defines functions independent of the source data being mapped into RDF, as resources within the mapping itself. Findings: The approach was evaluated using two typical real world use cases. We have compared how well our approach and others (that include transformation functions as part of the uplift mapping) could implement an uplift mapping from CSV/Tabular into RDF. This comparison indicates that our approach performs well for these use cases. Originality/value: This paper presents a comparison framework and applies it to the state of the art in CSV uplift tools. Furthermore, we describe FunUL, which unlike other related work, defines functions as resources within the uplift mapping itself, integrating data transformation functions and mapping definitions. This makes the generation of RDF from source data transparent and traceable. Moreover, since functions are defined as resources, these can be reused multiple times within mappings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An evaluation of uplift mapping languages

Crotti

Debruyne

Brennan

et al. 2017

IJWIS

View full text Add to dashboard Cite

show abstract

“…Embora possamos identificar vantagens para a operacionalização da informação na adoção dos princípios do Linked Data que possuem potencial para transformar a ciência e a educação superior, estes princípios não são amplamente empregados no contexto acadêmico (D'Aquin et al, 2013a;Linked Education, 2017). A colaboração científica busca há muito tempo prover maior compartilhamento e reuso de conhecimento e dados (Keßler, D'Aquin e Dietze, 2013), porém a maioria dos cientistas ainda vê a falta de acesso aos dados gerados por outros pesquisadores ou instituições como um grande impedimento ao progresso da ciência (Purohit et al, 2016). Ainda assim, é possível observar que o uso de recursos científicos e acadêmicos disponíveis por meio do Linked Data tem aumentado (D'Aquin et al, 2013a;Daga et al, 2016), justificando o interesse na continuação de iniciativas e demandando trabalhos que visem a publicação destes recursos na Web de Dados.…”

Section: Introductionunclassified

Linked Open Data no contexto acadêmico

Rozsa

Dutra

Nhacuongue

2017

BRAJIS

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ResumoO presente trabalho tem como objetivo identificar quais são os vocabulários Linked Data utilizados na descrição de recursos no contexto acadêmico. Esta é uma pesquisa descritiva, pois caracteriza os vocabulários identificados; utiliza uma abordagem quantitativa para definir indicadores de popularidade; e realiza a coleta de dados por meio de uma abordagem documental. Os vocabulários são identificados por meio de Web Sites e conjuntos de dados (datasets) no contexto acadêmico. Indicadores de popularidade são criados baseados na presença do vocabulário no Linked Open Vocabularies (LOV) e da quantidade de ocorrência dos vocabulários em datasets tanto dentro quanto fora do contexto acadêmico. Os datasets utilizados para a identificação de vocabulários foram encontrados em iniciativas Linked Data para o contexto acadêmico. Ao todo, foram identificados 102 vocabulários utilizados na descrição de dados e metadados para o contexto acadêmico, relacionados a variados tópicos: Pessoas e Organizações, Cursos e Formação, Recursos Acadêmicos e Institucionais, Projetos, Pesquisas, Coleções, Recursos de Multimídia, Páginas Web e Redes Sociais, Localização, Tempo e Eventos, Dados Estatísticos, Metadados e Outros. Assim, percebemos que a CI pode contribuir em diferentes vertentes relacionadas ao Linked Data, tais como na criação e disseminação de diretrizes sobre o uso de metadados, conciliadas a partir de práticas e estudos de usuário. AbstractThis work aims to identify the Linked Data vocabularies used to describe resources in academic contexts. It is a descriptive research, based on a quantitative approach to define indicators of popularity, as well as a documental approach for data gathering. We have identified vocabularies by means of academic-context-based Web Sites and datasets. We have proposed popularity indicators based on the presence of vocabularies in the LOV dataset (Linked Open Vocabularies). It has also been taken into account the amount of occurrence of vocabularies in datasets both inside and outside the academic contexts. We have identified the main datasets used to identify vocabularies in Linked Data initiatives for academic contexts. We have found 102 vocabularies used to describe data and metadata for academic contexts, related to various topics such as People and Organizations, Courses and Training, . In the end, we can conclude that Information Science can contribute in different aspects for the Linked Data approach, such as in the creation and dissemination of guidelines for metadata use, according to users' studies and practices.

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