Semantic Scientific Knowledge Integration
Papers from the AAAI Spring Symposium
Deborah L. McGuinness, Peter Fox, and Boyan Brodaric, Cochairs
Interest in and requirements for the next generation of information technology for science are expanding. e-Science has become a growing subject of discussion covering topics such as grid computing for science and knowledge-enhanced scientific data retrieval. Within individual science areas, we are experiencing the emergence of virtual observatories such as those in astronomy, heliophysics, geophysics and solar-terrestrial physics, where virtual distributed collections of scientific data are made available in a transparent manner. The goal of such efforts is to provide a scientific research environment that provides software tools and interfaces to interoperating data archives. While initial goals for these efforts may include relatively simple uses of AI techniques, the medium and long range goals for these efforts require full scale semantic integration of scientific data, thus they present interesting motivations for and tests of artificial intelligence techniques.
Concurrent with the growing demand for next generation information technology for science is a growth in semantic technologies. While knowledge representation languages and environments continue to evolve, some have reached a stable state in terms of reaching recommendation status from standards bodies. This recommendation status has attracted the interest of startup companies as well as established companies and a number off academic and commercial tools and environments are now available for use.