"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."
The University of Arizona is awarded a grant to develop tools that enable biologists to better integrate web data and services. The project has three specific goals. First, it will develop ontologies as the "currency" of semantic web services. Ontologies were historically built as static categorizations of knowledge, yet now need to be re-factored in order to use them effectively for data and service integration. Second, it will advance semantic searching as a method to transform discovery and service engagement in biology. This is achieved by extending semantic searching from explicit ontology subsumption assertions to full implicit subsumption relations. Semantic searching?the most innovative and powerful contribution of this work?unites data and services using ontologies into a searchable knowledge base complete with logical inference. Finally, it will integrate education, outreach, and training as an integral part of development in semantic web services. The project will carry out these activities in collaboration with St. John's College and the National Center for Genome Resources. Particularly for developers at major information resources, this training component introduces practitioners to the use and implementation of semantic web services. Access to the products of this grant will be available at http://sswap.info.
The approach is to recognize that for much of biology the information medium for data discovery, access, and assimilation is the World Wide Web. The World Wide Web means that data and algorithms?services?are, and will continue to be, distributed, heterogeneous, and often persistent, yet also dynamic and sometimes ephemeral; that integration on top of this informatics landscape means that we need to address fundamental issues in common syntaxes, shared semantics, and on-demand discovery. Semantic web services, and the ability to semantically search for resources and engage them have value beyond biology. In this manner, the work moves the web towards a distributed, logical network amenable to machine reasoning.
