The two main challenges for the coming to pass of the Semantic Web are scalability and distribution . The problem of scalability is that a well-designed web-oriented knowledge-based system must be able to handle larger and larger volumes of knowledge without unbearable degradation of performance. Dealing with the second challenge — distribution — is as complex an issue since it must deal efficiently and seamlessly with knowledge spread all over the Internet under “real-life” conditions (cache management, handling faulty connections and time delays, query distribution, etc.).

Our essential motivation in the CEDAR project is the systematic study of, and experimentation with, an approach to knowledge representation and processing that is an alternative to what has prevailed so far, such as Description Logic (DL) and its OWL implementations. DL's essential impediment facing the aforementioned challenges stems from its relying on an inflationary and non-incremental reasoning method (Tableaux).

The alternative formalism upon which the CEDAR project relies — Order-Sorted Feature (OSF) constraint logic — can provide a satisfactory solution to both challenges. This approach exploits the sorted-graph nature of data making up a knowledge-based system such as RDF, basing its reasoning on constraint-solving extending sorted-graph unification. Thus, contrary to mainstream DL-based SW reasoning, OSF knowledge processing is operationally lazy (i.e., it does not do anything that is not needed), incremental (it can process information as it materializes), endowed with instant (i.e., 0-cost) “memo-sorting” (viz., proof-caching sorts), and capable of handling very large concept taxonomies using modulated binary encoding. One of the most important objectives of the CEDAR project is to test OSF technology on existing benchmarks and realistic simulations.

This talk will present the essential of the OSF constraint calculus and summarize the tasks planned for the CEDAR project for the next two years.