KiWi as a Social Wiki Platform for Software Development, Open Ontology Management

KiWi – Knowledge in a Wiki, Day 2 – Josef Holy from Sun Microsystems Prague led the first part of today’s use case presentation. With the KiWi semantic wiki system (or: wiki on steroids, as Josef Holy put it), they want to be able to increase the productivity of knowledge workers. Sun Microsystems have extensive experience with online and community collaboration and they want Kiwi to become a social wiki platform that is deployable in various contexts, i.e. that ties in with other platforms such as Netbeans or Zembly.

One of Sun’s further assumptions is that users will migrate to KiWi neither immediately nor completely – and that’s an insight anyone developing yet another social platform should take to their heart. What was true in Field of Dreams – “If you build it, they will come” – does not quite apply here. The network effect works in favour of existing communities, and instead of striving to replace an existing platform, one might be better off with mashable contents and services.

The particular benefit of a semantic wiki is that it allows moving from unstructured to structured information (relatively) easily. For KiWi @ Sun (and in favour of mashed information), this means that what is relevant will be structured, both by people and by machines – a process that is going to extend beyond company boundaries. People will bring in structure by creating links from KiWi documents to external systems as well as by writing new facts (which the KiWi system will represent as triples) about external information. What is not relevant, won’t be structured – and will be forgotten. After all, it’s forgetting that makes you remember the important stuff.

One note about the users of KiWi at Sun: Since this use case focuses on knowledge management for software development, it can be taken for granted that users will have an above-average level of web savvyness. Primary users will be software designers (i.e. the people who design for the users of the final product) and developers – learn more about the different roles in a software development project at Sun here.

Consequently, the User Interface (UI) concept Josef introduced also comprises a social networking unit – things such as a ‘My Contacts’, ‘My Pages’ list, but most importantly an activity feed, which will help users to collaborate, participate, discover activities that others are currently working, develop a mental ‘social map’ of the community. Such an activity stream (similar to Facebook’s News Stream) would contain items such as:

• Szaby wrote a blog post
• Josef rated document XUI specs: five stars
• Peter created document ToDoList KiWi-UI
• Stephanie is now a contact of Marek
• Klara shared a document with Sebastian

Considering the target group, it is also planned that the UI will be extensible through widgets that users are able to write themselves.

*coffee break*

Above: The KiWi-Team, hailing (officially) from Austria, the Czech republic, Denmark and Germany

After the break, Andreas Blumauer (Semantic Web Company, Vienna) followed up with a talk entitled “Open Ontology Management & Linked Data” which explored the uses of the Web of Data for the Sun usecase.

His argument was that content and topic-centred, open communities should have mechanisms at their disposal for relating content and activities to particular parts of a shared concept model, e.g. of an ontology. In particular in projects like NetBeans, where contents and related processes evolve over time, different NetBeans groups utilizing the KIWI system should be allowed to maintain and share their own concept models. The combination of bottom-up and top-down approaches would, for instance, come as the combination of free tagging (where people often use different labels to refer to the same, or the same label to refer to different things) and concept tagging.

Free concepts can be turned into controlled ones, too, by being inserted into an existing controlled vocabulary, as either a narrower or related concept of any existing controlled concept. Open Ontology Management done this way is a Learning system: Through the combination of a Free Extraction Model (FEM) and a Controlled Extraction Model (CEM), text extraction improves over time.

Andreas also revealed a first glimpse of a project currently in stealth mode, code name ‘PoolParty’, which is an Open Ontology Management System that can be used to enrich local knowledge with data from the web. PoolParty consumes Linked Data and provides Linked Data; in the context of the current use case, it will be able to communicate with the KiWi System. Please contact Andreas if you would like to be notified about the further development of PoolParty.