Andy Seaborne

Jena: implementing the semantic web recommendations

The new Semantic Web recommendations for RDF, RDFS and OWL have, at their heart, the RDF graph. Jena2, a second-generation RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the RDF recommendations, such as containers and reification. The Ontology API includes support for RDFS and OWL, including advanced OWL Full support. Jena includes the de facto reference RDF/XML parser, and provides RDF/XML output using the full range of the rich RDF/XML grammar. N3 I/O is supported. RDF graphs can be stored in-memory or in databases. Jenas query language, RDQL, and the Web API are both offered for the next round of standardization.

SPARQL basic graph pattern optimization using selectivity estimation

In this paper, we formalize the problem of Basic Graph Pattern (BGP) optimization for SPARQL queries and main memory graph implementations of RDF data. We define and analyze the characteristics of heuristics for selectivity-based static BGP optimization. The heuristics range from simple triple pattern variable counting to more sophisticated selectivity estimation techniques. Customized summary statistics for RDF data enable the selectivity estimation of joined triple patterns and the development of efficient heuristics. Using the Lehigh University Benchmark (LUBM), we evaluate the performance of the heuristics for the queries provided by the LUBM and discuss some of them in more details.

Three Implementations of SquishQL, a Simple RDF Query Language

RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages [14] and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.

SWAN: A distributed knowledge infrastructure for Alzheimer disease research

SWAN - a Semantic Web Application in Neuromedicine - is a project to develop an effective, integrated scientific knowledge infrastructure for Alzheimer Disease (AD) researchers, enabled by Semantic Web technology and deployed on Alzforum (www.alzforum.org), a scientific web community for AD research. This infrastructure may later be deployed for research communities in other neuromedical disorders. SWAN incorporates the full biomedical research knowledge lifecycle in its ontological model, including support for personal data organization, hypothesis generation, experimentation, lab data organization, and digital pre-publication collaboration. Community, laboratory, and personal digital resources may all be organized and interconnected using SWANs common semantic framework.

An RDF NetAPI

This paper describes some initial work on a NetAPI for accessing and updating RDF data over the web. The NetAPI includes actions for conditional extraction or update of RDF data, actions for model upload and download and also the ability to enquire about the capabilities of a hosting server. An initial experimental system is described which partially implements these ideas within the Jena toolkit.

Using Hybrid Search and Query for E-discovery Identification

We investigated the use of a hybrid search and query for locating enterprise data relevant to a requesting partys legal case (e-discovery identification). We extended the query capabilities of SPARQL with search capabilities to provide integrated access to structured, semi-structured and unstructured data sources. Every data source in the enterprise is potentially within the scope of e-discovery identification. So we use some common enterprise structured data sources that provide product and organizational information to guide the search and restrict it to a manageable scale. We use hybrid search and query to conduct a rich high-level search, which identifies the key people and products to coarsely locate relevant data-sources. Furthermore the product and organizational data sources are also used to increase recall which is a key requirement for e-discovery Identification.