RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data.

XQuery is a query language designed to allow queries across the many kinds of information that are represented in XML. Although topic maps and RDF can also be represented in XML, many have held that their many possible syntactic forms make them extremely difficult to query using an XML query language, and that they can only be queried using special-purpose query languages with built-in knowledge of their semantics, including the ability to exploit RDF schema information. This talk shows that XQuery can, in fact, be used to solve the kinds of queries for which RDF and topic map query languages were designed, though with a loss of type safety.

Large-scale Semantic Web applications require large-scale storage of Resource Description Framework (RDF) information and a means to analyze that information via the Web Ontology Language (OWL) in near real time. The Kowari Metastore was designed as a purpose-built RDF database to fulfill this requirement. Kowari provides a scalable, transaction-safe storage infrastructure for RDF statements and an expressive query language for their analysis, with or without the use of a subset of the RDF Schema and/or OWL languages. OWL Lite plus the full cardinality constraints from OWL Full are currently supported via the interactive Tucana Query Language (iTQL) or the Simple Ontology Framework API (SOFA). Kowari’s native quad-store indexing scheme has been shown to scale to hundreds of millions of RDF statements on a single machine. Kowari is an Open Source project sponsored by Tucana Technologies and is licensed

Abstract. Tasked with designing a metadata management system for a large scientific data repository, we find that the customary database application development procedure exhibits several disadvantages in this environment. Data cannot be accessed until the system is fully designed and implemented, specialized data modeling skills are required to design an appropriate schema, and once designed, such schemas are intolerant of change. We minimize setup and maintenance costs by automating the database design, data load, and data transformation tasks. Data creators are responsible only for extracting data from heterogeneous sources according to a simple RDF-based data model. The system then loads the data into a generic RDBMS schema. Additional grouping structures to support query formulation and processing are discovered by the system or defined by the users via a web interface. Discovered and imposed structures constitute emergent semantics for otherwise disorganized information. 1

Abstract. Nowadays there is a great effort underway to improve the World Wide Web. A better content organisation, allowing automatic processing, leading to the Semantic Web is one of the main goals. In the light of bringing this technology closer to the Software Engineering community we propose an architecture allowing an easier development for ontology-based applications. Thus, we first present a methodology for ontology creation and automatic code generation using the widely adopted CASE UML tools. And based on a study of the art of the different RDF storage and querying systems, we couple this methodology with the Sesame system for providing a framework able to deal with large knowledge bases. 1

For guidance on citations see FAQs.