• Components
• Modules
• Wrapper

In short, components are pluggable controllers, modules are those little boxes OntoWiki has been using for some time now and wrapper are extension for extracting triples from external sources and importing them into your knowledge base.

One wrapper that ships with OntoWiki can e. g. load triples from Linked Data-enabled endpoints like Sindice or DBpedia. Among others, this feature is demonstrated in the new screencast. But the wrapper is only a part of OntoWiki’s new Linked Data enhancements. A plug-in publishes resources as linked data, provided their URI shares the prefix with the domain the respective OntoWiki installation runs under and the named graph is readable.

Besides extensibility, improvements in performance was one of the key goals for OntoWiki 1.0. Thus, it was only natural to make OntoWiki work with Virtuoso, one of the fastest RDF triple stores around. Besides Virtuoso, MySQL is still supported as well. It underwent, however, serious refactoring and is now based around ZendDb instead of ADOdb as its abstraction layer.

A preliminary version (OntoWiki 0.9) will be released in the coming weeks. It is based on the 1.0 code base but doesn’t contain all the features we’d like to include in 1.0.

We’ve prepared a brand new OntoWiki Screencast from the source trunk to show some special features of the new OntoWiki.

In particular, this screencast demonstrates

1. the model dashboard with current discussions,
2. discussions on specific resources,
3. how to create a new property on a resource,
4. how to link a local resource with an external one (from sindice), and
5. how to gather linked data from this external resource.

From a technical point of view, this screencast demonstrates our new RDFa widget library (thx to Norman Heino), our data wrapper system (thx to Philipp Frischmuth) and our new map plugin (thx to Nathanel Arndt).

The screencast is based on revision 3333 from the current trunk and uses the Virtuoso backend.

• a new learning algorithm (CELOE) designed specifically for extending OWL ontologies
• a Protege plugin using CELOE
• a manual for getting started using DL-Learner
• performance improvements through stochastic methods
• more learning examples, unit tests, code quality improvements

DL-Learner can be used to:

• solve general supervised Machine Learning problems using ontologies as background knowledge (given as OWL files, SPARQL endpoints, etc.), e.g. it was used to predict whether chemicals can cause cancer
• help knowledge engineers by learning definitions and subclass axioms (see the Protege plugin and another one for OntoWiki is in progress)
• generating user recommendations when browsing knowledge bases

I’d like to thank all contributors, in particular active developers and everyone who sent us valuable feedback.

• http://aksw.org/LSWT
• More Flickr pics