SBIR Phase II: Translational Information Management for Industry

这项小企业创新研究（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大量信息。员工需要了解过去的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。然而，拥有数千名员工的公司却拥有数百万份文件。手动索引它们以查找、分析和采取行动的成本高得令人望而却步。此外，超负荷的工人没有时间或培训来承担这项任务。兼并和收购加剧了这个问题。此外，多年来，公司积累大量重复和过期的文件是很常见的，员工不会花时间去合理化和删除这些文件。其结果是存储成本的膨胀和生产力的降低，因为工人们很难找到相关的、最新的信息。不一致的信息治理还将组织置于风险之中——诉讼（保留没有法律或业务价值的文档）、安全（使用过时的流程安全管理程序）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）很难定位和分析的问题，除非对它们进行分类和标记。但是对于大型集合来说，手动分类和标记过于昂贵且不一致。大公司存储数百万份文档。网上甚至有更多相关的信息。该研究的目标是提供软件助手，将文档分类为预先指定的类别，添加标签来描述每个文档的内容，以及文档中命名的实体（例如油田）。这些助手识别相关文档，并在网络上或公司电脑上出现感兴趣的新文档时发送警报，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，公司可以单独采用或作为一个整体来帮助可持续地管理信息。这些助手建立在提议的研究基础上，以开发和整合无监督机器学习、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，包括过载、查找相关的、最新的信息、分析非结构化信息以及确定不需要的文档以进行删除。

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 In addition, over years, it is common for companies to accumulate large numbers of duplicate and out-of-date documents that workers do not take the time to rationalize and delete.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also puts organizations at risk - litigation (retaining documents without legal or business value), safety (using out-of-date process safety management procedures) and operational (not leveraging best practices and lessons learned across the enterprise and beyond).This Small Business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