SBIR Phase I: Hybrid Question Answering Combining a Search Index with an RDF Store

SBIR 第一阶段：将搜索索引与 RDF 存储相结合的混合问答

• 批准号: 1113285
• 负责人: Christine Nezda
• 金额: $ 14.98万
• 依托单位: Lymba Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1113285&HistoricalAwards=false
• 关键词: SBIR; Phase; Hybrid; Question; Answering

This Small Business Innovation Research (SBIR) Phase I project will address the issue that enterprises today are faced with the problem of linking their disparate structured databases with unstructured text documents like articles, manuals, reports, emails, blogs, folksonomies, and others. There is no easy way to perform a federated search, let alone enable more intelligent applications over such diverse data sources without considerable time and effort spent in system and data model customization by experts. With the recent emergence of commercial grade Resource Description Framework (RDF) triple stores it becomes possible to merge massive amounts of structured and unstructured data by defining a common ontology model for the DBMS schemas and representing the structured content as semantic triples. Lymba proposes novel methods to transform unstructured data sources inside corporate firewalls into a consolidated RDF store, merge it with other ontologies and structured data, and moreover offer a natural language question answering (QA) interface for easy use. To make the QA robust, an innovative hybrid approach is proposed that draws answers from the RDF store as well as directly from indexed text documents. The potential impact of delivering a question answering system that operates on a commercial grade RDF store is significant as it fills a need for users of this store to easily access more information and quickly implement intelligent applications using natural language questions as the main vehicle. The proposal also leads to enabling technology software to advance the semantic web. If successfully deployed, the proposed research has the potential to translate into a viable commercial product with significant revenues.

这个小型企业创新研究 (SBIR) 第一阶段项目将解决当今企业面临的将不同的结构化数据库与文章、手册、报告、电子邮件、博客、大众分类法等非结构化文本文档链接起来的问题。没有简单的方法来执行联合搜索，更不用说在专家不花费大量时间和精力来定制系统和数据模型的情况下，在如此多样化的数据源上启用更智能的应用程序。随着最近商业级资源描述框架 (RDF) 三元组存储的出现，通过为 DBMS 模式定义通用本体模型并将结构化内容表示为语义三元组，合并大量结构化和非结构化数据成为可能。 Lymba 提出了新颖的方法，将企业防火墙内的非结构化数据源转换为统一的 RDF 存储，将其与其他本体和结构化数据合并，并提供易于使用的自然语言问答 (QA) 界面。为了使 QA 稳健，提出了一种创新的混合方法，该方法从 RDF 存储以及直接从索引文本文档中获取答案。提供在商业级 RDF 存储上运行的问答系统的潜在影响是巨大的，因为它满足了该存储的用户轻松访问更多信息并使用自然语言问题作为主要工具快速实现智能应用程序的需求。该提案还导致技术软件能够推动语义网络的发展。 如果成功部署，拟议的研究有可能转化为具有可观收入的可行商业产品。