In this paper we present SPREFQL, an extension of the SPARQL language that allows appending a PREFER clause that expresses "soft" preferences over the query results obtained by the main body of the query. The extension does not add expressivity and any SPREFQL query can be transformed to an equivalent standard SPARQL query. However, clearly separating preferences from the "hard" patterns and filters in the WHERE clause gives queries where the intention of the client is more cleanly expressed, an advantage for both human readability and machine optimization. In the paper we formally define the syntax and the semantics of the extension and we also provide empirical evidence that optimizations specific to SPREFQL improve run-time efficiency by comparison to the usually applied optimizations on the equivalent standard SPARQL query.
In the Semantic Web literature there have been proposed SPARQL extensions that feature the expression of preferences @cite_1 , typically transferring ideas and results from relational database frameworks much like the work presented here.
@cite_1
202256239 2134840053 2488578254 2401328826
The semantic web data and the SPARQL query language allow users to write precise queries. However, the lack of spatial information limits the use of the semantic web data on position-oriented query. In this paper, we introduce spatial SPARQL, a variant of SPARQL language, for querying spatial information integrated RDF data. Besides, we design a novel index SS-tree for evaluating the spatial queries. Based on the index, we propose a search algorithm. The experimental results show the effectiveness and the efficiency of our approach. The Semantic Web is based on accessing and reusing RDF data from many different sources, which one may assign different levels of authority and credibility. Existing Semantic Web query languages, like SPARQL, have targeted the retrieval, combination and reuse of facts, but have so far ignored all aspects of meta knowledge, such as origins, authorship, recency or certainty of data, to name but a few. In this paper, we present an original, generic, formalized and implemented approach for managing many dimensions of meta knowledge, like source, authorship, certainty and others. The approach re-uses existing RDF modeling possibilities in order to represent meta knowledge. Then, it extends SPARQL query processing in such a way that given a SPARQL query for data, one may request meta knowledge without modifying the original query. Thus, our approach achieves highly flexible and automatically coordinated querying for data and meta knowledge, while completely separating the two areas of concern. The last decade has witnessed an increasing interest in expressing preferences inside database queries. Even though most of the work has been devoted to relational databases, many proposals have also been made in the Semantic Web area in order to query RDF databases in a flexible way. This paper presents a survey of these approaches, classifies them, and points out research perspectives. In this paper, we present a temporal extension of the SPARQL query language for RDF graphs. The new language is based on a temporal RDF database model employing triple timestamping with temporal elements, which best preserves the scalability property enjoyed by triple storage technologies, especially in a multi-temporal setting. The proposed SPARQL extensions are aimed at embedding several features of the TSQL2 consensual language designed for temporal relational databases.