SBIR Phase I: Translational Information Management for Industry

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be improved effectiveness of document management systems in U.S. Businesses. The project integrates novel approaches to unsupervised machine learning, concept identification, and ontology construction to create a sustainable content management system that will allow companies to find and associate information more accurately and efficiently. Corporations run on information, and routine operations depend on finding information efficiently. For example, corporate acquisitions require filing information quickly in the acquiring company's systems; employee turnover necessitates intelligent analysis to enable continuing operations; and regulatory compliance and legal retention requirements demand consistent categorization and correct retention of records. While creating electronic documents is easy, finding and analyzing them remain difficult tasks. The proposed project is intended to provide effective assistance to companies, within everyday business practices, without requiring major investments in change. Distribution of information between corporate data centers and the cloud further necessitates tools to help with classification consistency and searchability. If successful, this project will provide an encompassing framework within which company workflows are integrated and corporate workers can more easily and efficiently extract usable information from corporate IT systems. This Small Business Innovation Research (SBIR) Phase I project provides new software tools for knowledge workers. Industrial information technology requires the integration of proven methods in a robust, sustainable framework. The investigators' prior work in artificial intelligence demonstrated that a well-designed framework, with open source packages and interstitial software, can provide an effective knowledge management system. In this project the company intends to mine and extend research ideas from knowledge management, artificial intelligence, natural language processing, machine learning, information retrieval and human-computer interfaces. Work in artificial intelligence has shown that domain knowledge is necessary for high performance problem solving. The company intends to leverage corporate knowledge to augment keyword search with semantics of the domain. Concept identification methods developed for natural language processing will be used to augment the powerful statistical tools provided by unsupervised machine learning and information retrieval technology.