State-of-the-art approaches for meta-knowledge assertion in the web of data

• Autor(a) principal: Sen, Sangeeta
• Data de Publicação: 2020
• Outros Autores: Malta, Mariana Curado, Dutta, Biswanath, Dutta, Animesh
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/17903
• topic: Meta-knowledge; Provenance; RDF; Semantic web; SPARQL; Graph data

The integration of meta-knowledge on the Web of data is essential to support trustworthiness. This is in fact an issue because of the enormous amount of data that exists on the Web of Data. Meta-knowledge describes how the data is generated, manipulated, and disseminated. In the last few years, several approaches have been proposed for tracing and representing meta-knowledge efficiently on a statement or on a set of statements in the Semantic Web. The approaches differ significantly; for instance, in terms of modelling patterns, the number of statements generation, redundancy of the resources, query length, or query response time. This article reports a systematic review of the various approaches of the four dimensions (namely time, trust, fuzzy, and provenance) to provide an overview of the meta-knowledge assertion techniques in the field of the Semantic Web. Some experiments are conducted to analyze the actual performance of the approaches of meta-knowledge assertion considering the provenance dimension. These experiments are based on specific parameters such as graph size, number of statements generation, redundancy, query length, and query response time. All the experiments are done with real-world datasets. The semantics of the different approaches are compared to analyze the methodology of the approaches. Our study and experiments highlight the advantages and limitations of the approaches in terms of the parameters mentioned above.