RESUMO
A considerable number of Internet of Things deployments are isolated from specific solutions, from devices to data platforms. Standardized data models were proposed to overcome the interoperability gap between these deployments. Next generation service interfaces-linked data (NGSI-LD) is one of the proposed platforms that exploits linked data and proposes an information model and an application programming interface (API) for easy use and standard management of context information. The NGSI-LD information model is based on JSON for Linked Data (JSON-LD) as a serialization format for context information. This efficiently exploits the potential of semantics and linked open data. However, the NGSI-LD graph API and query language are still theoretically defined and limited to some preliminary works. Consequently, current NGSI-LD implementations are mainly based on traditional databases, where the JSON-LD serialization is supported but not exploited owing to the difficulties in defining and implementing new NGSI-LD based Graph APIs. One of the basic solutions is the use of an RDF store for NGSI-LD payloads because these types of databases are well defined and maintained and will not need any added effort for JSON-LD based payloads. However, the main complication at this level is the use of reification to annotate relationships. This study focused on both aspects of exploiting the semantics of NGSI-LD by proposing standardized mapping mechanisms to RDF graphs without reifying JSON-LD payloads and with respect to the NGSI-LD context model and API. Our main proposals highlight that exploiting the RDF store for processing NGSI-LD data semantically is feasible and uncomplicated. We illustrated the proposed mapping approaches with real use-case examples and a possible exploitation of semantic approaches.
Assuntos
Web Semântica , Software , Bases de Dados Factuais , Internet , SemânticaRESUMO
La detección de comunidades se refiere al problema de identificar comunidades o particiones de nodos que comparten propiedades comunes en una red. Las redes de coautoría se consideran redes complejas, donde los nodos de la red son los autores, y los enlaces entre los nodos establecen la relación de coautoría en una o varias publicaciones. En los últimos años se han desarrollado investigaciones con el objetivo de publicar metadatos bibliográficos siguiendo los principios de los datos enlazados. Como resultado se obtienen grafos RDF que contienen los autores y las relaciones de coautoría que se establecen entre ellos. En este artículo se propone un método para la detección y visualización de comunidades en grafos RDF teniendo en cuenta las relaciones de coautoría como indicador para medir la colaboración científica. Con la implementación del método se pretende dotar a los especialistas en ciencias de la información de una herramienta de análisis que ayude en el proceso de toma de decisiones y la realización de estudios en esta área.
Community detection refers to the problem to identify communities or partitions of nodes that shares common properties in a network. The co-authorship networks are considered complex networks, where the nodes of the network are authors and the edges between nodes provides the co-authorship relationships in one or more publications. In recent years, researches have been carried out with the aim to publish bibliographic metadata following the principles of the linked data. Resulting from this, RDF graphs are obtained, containing the authors and the co-authorship relationships among them. In this paper we propose a method for detecting and visualizing communities in RDF graphs, considering co-authorship relationships as an indicator to measure scientific collaboration. With the implementation of the method proposed, we provide an analysis tool for specialists in information sciences, which improve the process of decision making and implementation of studies in the area.
A detecção de comunidades refere-se ao problema de identificar comunidades o partições de nodos que partilham propriedades comuns em uma rede. As redes de coautoria são consideradas redes complexas, onde os nodos da rede são os autores, e os enlaces entre os nodos estabelecem a relação de coautoria em uma ou várias publicações. Nos últimos anos se têm desenvolto investigações com o objetivo de publicar metadados bibliográficos seguindo os princípios dos dados ligados. Como resultado se obtêm grafos RDF que contêm os autores e as relações de coautoria que se estabelecem entre eles. Neste artigo se propõe um método para a detecção e visualização de comunidades em grafos RDF tendo em conta as relações de coautoria como indicador para medir a colaboração científica. Com a implementação do método se pretende dotar aos especialistas em ciências da informação de uma ferramenta de análise que ajude no processo de toma de decissões e a realização de estudos nesta área.
RESUMO
La detección de comunidades se refiere al problema de identificar comunidades o particiones de nodos que comparten propiedades comunes en una red. Las redes de coautoría se consideran redes complejas, donde los nodos de la red son los autores, y los enlaces entre los nodos establecen la relación de coautoría en una o varias publicaciones. En los últimos años se han desarrollado investigaciones con el objetivo de publicar metadatos bibliográficos siguiendo los principios de los datos enlazados. Como resultado se obtienen grafos RDF que contienen los autores y las relaciones de coautoría que se establecen entre ellos. En este artículo se propone un método para la detección y visualización de comunidades en grafos RDF teniendo en cuenta las relaciones de coautoría como indicador para medir la colaboración científica. Con la implementación del método se pretende dotar a los especialistas en ciencias de la información de una herramienta de análisis que ayude en el proceso de toma de decisiones y la realización de estudios en esta área(AU)
Community detection refers to the problem to identify communities or partitions of nodes that shares common properties in a network. The co-authorship networks are considered complex networks, where the nodes of the network are authors and the edges between nodes provides the co-authorship relationships in one or more publications. In recent years, researches have been carried out with the aim to publish bibliographic metadata following the principles of the linked data. Resulting from this, RDF graphs are obtained, containing the authors and the co-authorship relationships among them. In this paper we propose a method for detecting and visualizing communities in RDF graphs, considering co-authorship relationships as an indicator to measure scientific collaboration. With the implementation of the method proposed, we provide an analysis tool for specialists in information sciences, which improve the process of decision making and implementation of studies in the area(AU)
