Hi Mohd,
we have got a KM research group working at the La Salle university in Barcelona, Spain with a total of 4 final thesis students. We are working currently for two customers, a small strategic consulting company and the HP Professional Printers division.
For the consulting company we basicly develop a new filestorage module with template documents for each project phase.
For HP we develop an "Expert Finder" module, which is basicly a search engine, where we check for the authors of the documents that match a search query. This project involves the classification of keywords, persons, documents and workgroups into an ontology, in order to restrict the search space and to desambiguate "names".
Name disambiguation is especially important, because names can refer to many persons, but it is vast majority of the information we've got. So if have got users classified into the ontology, and we've got a document classified, then we can determine the probability that a name in the document refers to one user or another.
We use the probabilistic calculus as base for our system, with ontology classification represented as a vector of probabilities, one probability for each node. This representation is very database-friendly, even though the size of the ontology gets somewhat limited.
Does this match a bit with your ideas?
We've got a lot of experience with description logics and frame-based symbolic systems, but we are going now for the probabilistic calculus, because it is closer to the scoring algorithms used in Internet search engines while providing sound semantics.
It would be definitely be interesting to see if we could find common ideas and or common projects.
Bests,
Frank
mailto:frank.bergmann_at_project.open_dot_com
http://www.project-open.com/
