SBIR Phase II: Customizable Question Answering System for Homeland Security and Commercial Applications

这个小企业创新研究（SBIR）第二阶段项目将产生一种新的问答技术。 该技术的特点如下：（1）自动过滤问题。 在第一阶段，语言计算机公司（LCC）开发了一个系统，该系统将高级问题分解为低层次的寻求事实的问题。 然而，其中一些问题被证明是荒谬的。 在第二阶段，公司建议将所有分解的问题提交给基于知识的系统，这将消除与隐性知识不一致的问题。 所有通过过滤的问题都将被传递回问答系统进行处理。 (2)将领域本体与大型参考本体对齐。 在第一阶段，LCC开发了一个从原始文本生成领域本体的工具。 在第二阶段，该公司将扩展该工具，使领域本体自动与一个总体领域独立的本体对齐。 这种对齐将允许查询概念的更深层次扩展，因为它将允许用依赖于域的内容来扩充独立于域的概念。 (3)语义关系的形式化评价。 问答系统的基础是从查询和文档中提取语义关系。 这些关系将被评估，以评估每一个问题回答的相对贡献。 评估的结果将确定语义的哪些方面对问答系统最有用。该项目将在以下方面产生直接影响：（1）该系统可部署在商业和政府环境中，检索信息的准确性，覆盖范围，可靠性和可用性至关重要。 该技术的理想应用包括国土防御，CRM，教育，医学和法律。 (2)该系统弥补了独立于域的内容和特定于域的内容之间的差距。 领域本体是自动构建的，并且这些本体自动与大型参考本体对齐，使得查询可以同时扩展为适合于许多不同领域的术语。

This Small Business Innovation Research (SBIR) Phase II project will result in a novel question-answering technology. The features of this technology are as follows: (1) Automatic filtering of questions. During Phase I, Language Computer Corporation (LCC) developed a system that decomposes high-level questions into low-level, fact-seeking questions. Some of these questions, however, turn out to be nonsensical. In Phase II, the firm proposes to submit all of the decomposed questions to a knowledge-based system, which will eliminate questions that are inconsistent with tacit knowledge. All of the questions that survive filtering will be passed back for processing by the question-answering system. (2) Aligning domain ontologies with a large reference ontology. During Phase I, LCC developed a tool that generates domain ontologies from raw text. During Phase II, the firm will extend this tool so that the domain ontologies are automatically aligned with an overarching domain-independent ontology. This alignment will permit deeper expansion of query concepts, because it will allow domain-independent concepts to be augmented with domain-dependent content. (3) Formal evaluation of semantic relations. The foundation of the question-answering system is semantic relations extracted from queries and documents. These relations will be evaluated to assess the relative contribution of each one to question answering. The result of this evaluation will establish which aspects of semantics are most useful to question- answering.This project will have a direct impact in the following areas: (1) The system can be deployed in commercial and government settings where the accuracy, coverage, reliability, and usability of the retrieved information are crucial. Ideal applications for the technology include homeland defense, CRM, education, medicine, and the law. (2) The system bridges the gap between domain-independent and domain-specific content. Domain ontologies are constructed automatically, and these ontologies are automatically aligned with a large reference ontology, so that queries can be simultaneously expanded into the terms appropriate to many different domains.