SBIR Phase II: Translational Information Management for Industry

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/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海量信息。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。手动为它们编制索引，以便发现、分析和操作它们，成本高得令人望而却步。此外，超负荷工作的工人没有承担这项任务所需的时间或培训。并购活动加剧了这一问题。此外，多年来，公司积累了大量重复和过时的文档，工人们没有花时间对这些文档进行合理化和删除，这是很常见的。其结果是存储成本膨胀，工作人员努力寻找相关的最新信息，从而降低了生产率。不一致的信息治理还会使组织面临风险--诉讼(保留没有法律或商业价值的文档)、安全(使用过时的流程安全管理程序)和运营(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--很难找到和分析的问题，除非对它们进行分类和标记。但是，人工分类和标记对于大型集合来说太昂贵且不一致。大公司存储数以百万计的文件。而且网上还有更多的相关信息。拟议研究的目标是提供软件助理，将文件分类为预先指定的类别，添加标签以描述每份文件的内容，以及文件中命名的实体(例如油田)。当新的感兴趣的文件出现在网络或公司的电脑上时，助手通过发送警报来识别相关文件，并帮助人们了解新的发展。主要的技术成果将是一套软件助手，公司可以单独采用，也可以作为整体采用，以帮助可持续地管理信息。这些助手以拟议的研究为基础，开发和集成无监督机器学习、概念识别和本体构建的新方法。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、分析非结构化信息以及识别要删除的不需要的文档。

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 In addition, over years, it is common for companies to accumulate large numbers of duplicate and out-of-date documents that workers do not take the time to rationalize and delete.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also puts organizations at risk - litigation (retaining documents without legal or business value), safety (using out-of-date process safety management procedures) and operational (not leveraging best practices and lessons learned across the enterprise and beyond).This Small Business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.