SBIR Phase I: Translational Information Management for Industry

SBIR 第一阶段：行业翻译信息管理

• 批准号: 1415757
• 负责人: Bruce Buchanan
• 金额: $ 14.38万
• 依托单位: i2k Connect LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1415757&HistoricalAwards=false
• 关键词: SBIR; Phase; Translational; Information; Management

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.

这项小企业创新研究（SBIR）第一阶段项目的广泛影响/商业潜力将提高美国企业文件管理系统的有效性。该项目集成了无监督机器学习、概念识别和本体构建的新方法，以创建一个可持续的内容管理系统，使公司能够更准确、更有效地查找和关联信息。公司依靠信息运转，日常运作依赖于有效地找到信息。例如，企业收购需要在收购方的系统中快速归档信息；员工流动需要智能分析，以确保持续运营；法规遵从性和法律保留要求要求对记录进行一致的分类和正确的保留。虽然创建电子文档很容易，但查找和分析它们仍然是一项艰巨的任务。建议的项目旨在在日常业务实践中为公司提供有效的帮助，而不需要在变更方面进行重大投资。企业数据中心和云之间的信息分布进一步需要工具来帮助实现分类一致性和可搜索性。如果成功的话，这个项目将提供一个包围性的框架，在这个框架中，公司的工作流程被集成，公司的工作人员可以更容易、更有效地从公司的IT系统中提取可用的信息。这个小企业创新研究（SBIR）第一阶段项目为知识工作者提供了新的软件工具。工业信息技术需要在一个强大的、可持续的框架中集成经过验证的方法。研究人员先前在人工智能方面的工作表明，一个设计良好的框架，加上开源软件包和插入式软件，可以提供一个有效的知识管理系统。在这个项目中，公司打算从知识管理、人工智能、自然语言处理、机器学习、信息检索和人机界面等方面挖掘和扩展研究思路。人工智能领域的研究表明，领域知识是高效解决问题的必要条件。该公司打算利用企业知识来增强具有领域语义的关键字搜索。为自然语言处理开发的概念识别方法将用于增强由无监督机器学习和信息检索技术提供的强大统计工具。