-------- Original-Nachricht -------- Betreff: [computational.science] CFP: SEMANTIC SCIENTIFIC KNOWLEDGE INTEGRATION AAAI Spring Symposium Series Workshop; March 26-28; Papers Due Nov 2 Datum: Sat, 06 Oct 2007 10:54:16 -0700 Von: Deborah L. McGuinness dlm@ksl.stanford.edu Organisation: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com

Call for papers and panel proposals. Apologies for multiple postings.

SEMANTIC SCIENTIFIC KNOWLEDGE INTEGRATION AAAI Spring Symposium Series Workshop MARCH 26-28, 2008, STANFORD UNIVERSITY., CALIFORNIA http://www.ksl.stanford.edu/people/dlm/sss08/

Organizers: Deborah L. McGuinness, Peter Fox, Boyan Brodaric (sski2008_organizers@ucar.edu)

We welcome full papers, extended abstracts, position statements, as well as panel proposals (indicating the names, affliations and email addresses for all panelists). Email two to six-page submissions in PDF format to sski2008_submissions@ucar.edu. Submissions will be judged on technical merit and on potential to generate discussion and create community collaboration. The organizers will prepare a technical report summarizing the workshop. Additionally, the organizers plan to organize a special issue of a journal on the same topic: selected authors of the best papers will be invited to submit a longer version of their paper for consideration in this special issue.

Important Dates (see web site http://www.ksl.stanford.edu/people/dlm/sss08/for further dates/details):

Nov. 2 - submission deadline (note: this is later than the general AAAI SSS default submission date) Nov. 30 - review deadline Jan. 25 - Camera Ready submission to AAAI (hard deadline)

Description: 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. In this workshop, we are interested in bringing together the semantic technologies community with the scientific information technology community in an effort to build the general semantic science information community. The workshop has multiple goals including obtaining requirements for AI researchers from the scientific community, informing the computational science community of AI research efforts that are ready for use now or with additional research, and providing a forum for current collaborative efforts to present their work.

Topics: Knowledge representation foundations for e-science, Ontologies and ontology environments aimed at science integration applications, Knowledge provenance / meta data /annotation for e-science, AI-based scientific workflow, AI-supported virtual observatories, AI-supported community and collaboration for scientific application, Knowledge-based extraction of scientific data and data models, AI-based scientific interoperability, Scientific semantic web services, AI-supported scientific grid computing. Query languages for science, AI-based mapping and merging of scientific schemas

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