SBIR Phase II: Translational Information Management for Industry

这个小型企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力将是更有效和高效地使用非结构化数据。 将分析局限于结构化数据会忽略报告、备忘录、文章和其他书面文档中的大量信息。 员工需要有关过去工作和正在进行的项目、最佳实践、当前事件以及竞争对手和客户活动的信息。 然而，拥有数千名员工的公司拥有数百万份文件。 人工编制索引以便查找、分析和采取行动的成本高得令人望而却步，而且，超负荷工作的工人没有时间或培训来承担这项任务。 合并和收购加剧了这一问题。 此外，多年来，公司积累大量重复和过时的文档是很常见的，员工不会花时间合理化和删除这些文档。 其结果是存储成本膨胀和生产力下降，因为工人们很难找到相关的最新信息。 不一致的信息治理也会使组织面临风险-诉讼（保留没有法律的或商业价值的文件），安全（使用过时的工艺安全管理程序）和操作（没有利用企业内外的最佳实践和经验教训）。这个小型企业创新研究（SBIR）第二阶段项目解决了文本文档（尤其是组织内部的文本文档）很难定位和分析，除非它们被分类和标记。 但是手工分类和标记对于大型收藏来说太昂贵而且不一致。 大公司存储数百万个文档。 网上还有更多相关信息。 所提出的研究的目标是提供软件助手，其将文档分类为预先指定的类别，添加标签以描述每个文档是关于什么的，以及文档中命名的实体（例如，油田）。 这些助理识别相关文件，并在网络或公司计算机上出现感兴趣的新文件时发送警报，帮助人们了解新的发展情况。 主要的技术成果将是一套软件助手，公司可以单独或整体采用，以帮助可持续地管理信息。 这些助理建立在拟议的研究，开发和集成新的方法，无监督机器学习，概念识别和本体构建。 它们将使公司能够克服主要问题，包括超载，查找相关的最新信息，分析非结构化信息，并确定不需要的文件进行删除。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be more effective and efficient use of unstructured data. Limiting analysis to structured data ignores the massive amount of information in reports, memos, articles, and other written documents. Workers require information on past work and ongoing projects, best practices, current events and competitor and customer activities. However, companies with thousands of workers have millions of documents. It is prohibitively expensive to index them manually so that they can be found, analyzed and acted on. Moreover, overloaded workers do not have the time or training required to take on the task. The problem is exacerbated by mergers and acquisitions. In addition, over years, it is common for companies to accumulate large numbers of duplicate and out-of-date documents that workers do not take the time to rationalize and delete. The result is inflated storage costs and reduced productivity as workers struggle to find the relevant, up-to-date information. Inconsistent information governance also puts organizations at risk - litigation (retaining documents without legal or business value), safety (using out-of-date process safety management procedures) and operational (not leveraging best practices and lessons learned across the enterprise and beyond).This Small Business Innovation Research (SBIR) Phase II project addresses the problem that text documents - especially those internal to an organization - are very difficult to locate and analyze unless they are classified and tagged. But manual classification and tagging are too expensive and inconsistent for large collections. Large companies store many millions of documents. And there is even more relevant information on the Web. The objective of the proposed research to is to provide software assistants that classify documents into pre-specified categories, add tags to describe what each document is about, and the entities named in the documents (e.g., oilfields). The assistants identify relevant documents and help people to learn of new developments by sending alerts when new documents of interest appear on the web or in the company's computers. The primary technical result will be a suite of software assistants that companies can adopt singly or as an ensemble to help manage information sustainably. These assistants build upon the proposed research to develop and integrate novel approaches to unsupervised machine learning, concept identification, and ontology construction. They will enable companies to overcome major problems, including overload, finding relevant, up-to-date information, analyzing unstructured information, and identifying unneeded documents for elimination.