Automatic Generation of Machine Readable Context Annotations for SPARQL Results
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In this paper, we propose an approach to generate machine readable context annotations for SPARQL Results. According to W3C Recommendations, the retrieved data from RDF or OWL data sources are represented in tabular form, in which each cell’s data is described by only type and value. The simple query result form is generally useful, but it is not sufficient to explain the semantics of the data in query results. To explain the meaning of the data, appropriate annotations must be added to the query results. In this paper, we generate the annotations from the basic graph patterns in user’s queries. We could also manipulate the original queries to complete the annotations. The generated annotations are represented using the RDFa syntax in our study. The RDFa expressions in HTML are machine-understandable. We believe that our work will improve the trustworthiness of query results and contribute to distribute the data to meet the vision of the Semantic Web.
Abstract
In this paper, we propose an approach to generate machine readable context annotations for SPARQL Results. According to W3C Recommendations, the retrieved data from RDF or OWL data sources are represented in tabular form, in which each cell’s data is described by only type and value. The simple query result form is generally useful, but it is not sufficient to explain the semantics of the data in query results. To explain the meaning of the data, appropriate annotations must be added to the query results. In this paper, we generate the annotations from the basic graph patterns in user’s queries. We could also manipulate the original queries to complete the annotations. The generated annotations are represented using the RDFa syntax in our study. The RDFa expressions in HTML are machine-understandable. We believe that our work will improve the trustworthiness of query results and contribute to distribute the data to meet the vision of the Semantic Web.
참고문헌 (24)
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