In 2002, U.S. industry and government spent an estimated $285 billion on R&D according to National Institute of Standards and Technology (NIST), and spending is increasing. To get the most value from available funding, R&D project planners and researchers in all fields of technology need to locate, organize, and analyze knowledge that is relevant to their interests. These tasks are becoming increasingly challenging due the rapidly escalating amount of information and data on the Web. The Internet is an invaluable tool for instant access to information. However, while it's a time saver having information at your fingertips, is it also a time waster? One company thinks so and has been awarded funding through NIST's Advanced Technology Program (ATP) to develop an automated Web-searching and data organizing software system that gives a user exactly the information he wants from an Internet query.

InRAD LLC (Knoxville, Tenn.), the company that received ATP funding and that will develop the software, couldn't attract financing from venture-capital and incubator organizations. These organizations believed that there wasn't enough progress made to build a prototype, making it too risky to invest in. According to NIST, the project presents a high technical risk because integrating ontologies with machine-learning techniques has never been attempted.

Two crucial project subcontractors are Knowledge Based Systems (College Station, Tex.) and Sarnoff Corp. (Princeton, N.J.). Knowledge Based Systems will develop the AKDS toolset subsystem, which combines machine-learning techniques (text and data mining) with domain ontologies to enable ontology-assisted knowledge extraction from a variety of information sources. Sarnoff Corp. will develop the search subsystem, which performs targeted searches in domain-specific databases and trusted sites on the Internet for knowledge related to specific user requirements.

The successfully development of AKDS will allow rapid collection and organization of substantial quantities of relevant content from many available sources, avoid duplication of research efforts and help organizations better direct their R&D.