“My expectations for this project are that we are going to acquire experience in system development, learn more about social software, deepen our knowledge of information extraction and gain more insight into personalisation adaptation.” This unassuming statement is not coming from me (who is definitely going to learn a lot through KIWI), but from FranÃ§ois Bry, Professor in the Teaching and Research Unit ‘Programming and Modelling Languages’ in the Dept. of Computer Science at Ludwig-Maximilians-UniversitÃ¤t Munich. Their areas of expertise are in Automated Reasoning, Rule-based Query Languages, Event-Condition-Action Rule Languages and Web Information Systems – naturally, this is also going to be the area in which they are contributing to KIWI, i.e. developing enabling technologies in the area of reasoning, querying and reason maintenance. You have probably already heard of the REWERSE – Reasoning on the Web project, which is another project that FranÃ§ois and his team have been involved with.
The people on FranÃ§ois’ KIWI team are: Norbert Eisinger, a senior researcher at the research unit for programming and modelling languages, Klara Weiand, a German doctoral student who did her master’s thesis in the Netherlands, Jakub Kotowski, a doctoral student hailing from Prague, and Ingeborg v. Troschke, supporting the team as an administrative assistant. Jakub wrote his master’s thesis about ontology engineering at Charles University, Prague. While working at Sun he developed a prototype for a semantic-web based project tracking tool. Klara put a focus on Artifical Intelligence, Computational Linguistics and Cognitive Psychology when studying toward a BSc at the University of OsnabrÃ¼ck, further pursueing this interest when doing an MSc in Artificial Intelligence with a focus on Language and Speech Processing at the University of Amsterdam.
The goals of LMU’s contribution to KIWI are:
- to develop a rule-based language that can be used by wiki users to specify queries and derivation rules, ideally in a simple and intuitive way
- to develop a reason maintenance component for this language that gives users an opportunity to understand why derivations exist and that allows for versioning of updates of the knowledge base