KMi Seminars
An Open Rating System for Collaborative Ontology Evaluation
This event took place on Thursday 31 January 2008 at 11:30

 
Holger Lewen AIFB, Universität Karlsruhe (TH)

Open Rating Systems provide a means for obtaining many opinions on content from different people. The basic idea is that when seeking advice, people turn to someone they trust and whose opinion they value. Based on statements about the perceived trust towards the ability of another user to provide helpful reviews, reviews will be presented in an order that is inferred to be most useful for the user. Basic Open Rating Systems are currently employed when products should be ratable for end-users, like Amazon's product reviews.

In this talk an adapted Open Rating System model is presented that is tailored to ontology reviewing. It will be shown how such a system can be seen used as an ontology evaluation framework that can combine automated and human reviews. Also the status of the ongoing effort of integration with Watson will be discussed.

 
KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Social Software is...


Social Software
Social Software can be thought of as "software which extends, or derives added value from, human social behaviour - message boards, musical taste-sharing, photo-sharing, instant messaging, mailing lists, social networking."

Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.