BigOWLIM 3.4 Released - delivering Jena integration, geo-spatial indices and OWL2-QL support

[Apologies for cross-posting]


BigOWLIM 3.4 Released - delivering Jena integration, geo-spatial indices
and OWL2-QL support

Ontotext are pleased to announce version 3.4 of their OWLIM family of
semantic repositories. This release is aimed at improving the
integration possibilities for BigOWLIM by including support for the
popular Jena RDF framework, as well as improving the range of
application domains by including special extensions for Geo-spatial
data. Many other enhancements and updates are also included - full
details of these changes for BigOWLIM are given below:


      * Jena adapter (BETA): Applications which use the Jena framework
        or Jena-compliant RDF stores can seamlessly switch to BigOWLIM
        to take advantage of efficient loading and high-performance
        reasoning. At the same time, Jena's ARQ engine allows BigOWLIM
        to handle the latest SPARQL 1.1 extensions, e.g. aggregates. The
        adapter is still a beta version and has not been rigorously
        tested for conformance yet, but can be used with Joseki to make
        queries and has successfully passed BSBM and LUBM benchmarks.
        The results suggest that for most of the scenarios and tasks
        BigOWLIM can deliver considerable performance improvements when
        used as a replacement for Jena's own native RDF backend TDB. 
      * Geo-spatial extensions: Applications can efficiently make
        queries involving constraints such as 'nearby point' and 'within
        region'. Special-purpose indices allow such constraints to be
        evaluated very efficiently on top of large volumes of
        location-related data, for example, finding airports within 50
        miles of London in the GeoNames dataset (92 million statements,
        describing more than 6 million geographic features all over the
        world) becomes 500 times faster when compared to the same query
        evaluated without the geo-spatial indices. 
      * OWL2-QL support: This OWL2 profile is based on DL-LiteR, a
        variant of DL-Lite that does not require the unique name
        assumption. It is designed to be amenable to implementation on
        relational databases, due to its suitability for re-writing
        queries to SQL. This release includes a rule-set for this
        profile in order to expand the range of standard rule-sets and
        to give users more flexibility when choosing a balance between
        complexity of inference and scalability. 
      * Rule engine enhancements and improved reasoning performance: The
        rule-engine now supports the ability to use context as part of
        rule premises and consequences. This allows for more efficient
        processing of certain OWL constructions, particularly those
        rules using RDF lists. All predefined rule-sets have been
        upgraded to make use of this new expressiveness. As a result,
        there is now just a single rule-set for OWL2-RL, where in the
        previous version there was a 'conformant' and a 'reduced'
        version. The new rule engine has lead to an improvement in LUBM
        loading performance of around 22%. 
      * Enhanced Lucene-based full text search: More flexibility is
        enabled when using Lucene full-text search. Users can create
        multiple customised indices and can decide whether to include
        URIs or literals, select literals by language tags, and use
        custom analyzers and scorers. Any number of custom indices can
        be used within the same query. 
      * Auto-restore: A configurable policy parameter can be used to
        specify how the user wishes the repository to start after an
        abnormal termination. By default, the database restorer tool
        will be run automatically to return the database to the state
        prior to the stop event, i.e. to the state after the last
        committed transaction. 
      * Simplified 'implicit-only' statement retrieval: When using the
        Sesame openRDF API to return only implicit statements, the
        'implicit' pseudo-graph is now used. This is simpler and more
        consistent with query processing than the old method of invoking
        RepositoryConnection.getStatements() twice. 
      * Documentation: The distribution package includes two new guides:
        Replication Cluster Quick Start Guide that has details on
        installing and configuring a cluster and Performance Tuning
        Guide that brings together all information for optimising
        loading time, inference and query processing.


The OWLIM Team, November 2010