Banner Research
The KR group in our Department investigates modelling and representation of different forms of knowledge and reasoning, as found in a large variety of AI systems. We have an interest in both applications and theory. We study theoretical properties of knowledge representation and reasoning formalisms, but are also involved in developing practical knowledge-based systems. Recently, we have been very active in developments around the Semantic Web. 

Header Current Projects

Large Knowledge Collider
The EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider, a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web.
Involved: Frank van Harmelen, Annette den Teije, Zhisheng Huang,  Spyros Koutalis, Jacopo Urbani, and Gaston Tagni
Partners: STI Innsbruck,  Sheffield, CyCEuro, WICI, MPG, CEFRIEL, Siemens, WHO, Saltlux
Duration: 2008-2011

Linked Open Data Around-the-Clock
With the Linked Open Data Around-The-Clock (LATC) Support Action we want to enable you to leverage the Linking Open Data cloud for research and product purposes by:

  1. Deploying a 24/7 infrastructure to continuously monitor and improve the quality of data links within the Linking Open Data cloud.
  2. Lowering the access-barrier for data publishers as well as data consumers. We assemble and maintain a library of open source Linked Data tools and provide a data source inventory to the community.
  3. Creating and maintaining an in-depth test-bed for data intensive applications by publishing datasets produced by the European Commission, the European Parliament, and other European institutions as Linked Data on the Web and by interlinking them with other governmental data.
  4. Supporting both institutions as well as individuals with tutorials and best practices concerning Linked Data publication and consumption.

Involved: Frank van Harmelen,  Christophe Gueret
Partners: DERI, Talis, InfAI, FUB

Semantically Mapping Science
Scientometrics is the field of Social Sciences that studies the evolution of scientific fields: how they grow, shrink, merge, appear or dissapear, if they are inward- or outward-looking, how they are clustered, if they have a high or low in- and outflux of people etc. Typically, Scientometrics studies are done on the basis of bibliometric data: co-citation patterns, co-authoring pattersn, citation-impact studies, etc. The field has progressed rapidly since the widespread on-line availability of such bibliometric data (in the last 15 years or so). Such studies can now be done routinely. However, publishing is only one of the many activities of scientists. They also do things like: review papers, have discussions, change jobs, interact with companies, organise and participate in events, are members of boards (conference, professional organisations), etc. With the advent of the Web, these other activities of scientists now also leave on-line traces that can be used for scientometrics purposes. The question is: Can we use Semantic Web techniques to meaningfully detect, retrieve and manipulate such web-traces of activities of scientists in order to improve Scientometrics studies?
The project is funded by the Rathenau Institute.

BATNA Establishment using Semantic Web Technology
Laymen can turn to legal professionals to determine their legal position, but often resolve their disputes in an informal way, e.g. by mediation, negotiation. In the BEST-project (Batna Establishment using Semantic web Technology) we strive to provide disputing parties with information about their legal position in a liability case. The BEST-project aims to build a system that provides this information in the stage before they seek professional assistance. The target users of the system are laymen in the field of law, who want to get an insight into the legal aspects of their dispute. User queries are posed in laymen terms, and are translated to a legal characterisation of a case by means of vocabulary mappings. This legal characterisation is then used to search across the corpus of case law published by the Netherlands Council for the Judiciary. The BEST-project is funded under grant number 634.000.436 by the Netherlands Organisation for Scientific Research.