The tree-based languages XQuery and XSLT for XML are widely supported. Many tools do not yet support the new RDF graph query language SPARQL. We propose to embed SPARQL subqueries into XQuery/XSLT, such that XQuery and XSLT benefit from the graph query language constructs of SPARQL, and SPARQL benefits from features of XQuery/XSLT, which SPARQL does not support. The embedding enables XQuery/XSLT tools to handle at the same time XML queries and SPARQL subqueries, and XML and RDF data.

No abstract

No abstract

No abstract

No abstract

No abstract

The social web is becoming increasingly popular and important, because it creates the collective intelligence, which can produce more value than the sum of individuals. The social web uses the Semantic Web technology RDF to describe the social data in a machine-readable way. RDF query languages play certainly an important role in the social data analysis for extracting the collective intelligence. However, constructing such queries is not trivial since the social data is often quite large and assembled from a large number of different sources. In order to solve these challenges, we develop a Visual Query System (VQS) for helping the analysts of social data and other semantic data to formulate such queries easily and exactly. In this VQS, we suggest a condensed data view, a browser-like query creation system for absolute beginners and a Visual Query Language (VQL) for beginners and experienced users. Using the browser-like query creation or the VQL, the analysts of social data and other semantic data can construct queries with no or little syntax knowledge; using the condensed view, they can determine easily what queries should to be used. Furthermore, our system also supports precise suggestions to extend and refine existing queries.

Since there are a lot of similar or common properties between RDF and relational databases and between SPARQL and SQL, many efforts focus on leveraging the research results of optimizing relational query languages for optimizing SPARQL queries. However, SPARQL has its own characteristics different from SQL, which are not fully exploited by existing work. Therefore, there is still much space for research on optimizing SPARQL queries. Based on the triple nature of RDF data, we create 7 indices to retrieve RDF data quickly; based on the SPARQL-specific properties and the 7 indices, we develop a new, efficient approach to computing join by dynamically restricting triple patterns. Our experimental results show the efficiency of our approach.