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

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问题，即将不同的结构化数据库与非结构化文本文档（如文章，手册，报告，电子邮件，博客，大众分类法等）链接起来。没有简单的方法来执行联合搜索，更不用说在这样多样化的数据源上启用更智能的应用程序，而不需要专家在系统和数据模型定制上花费大量的时间和精力。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模式定义一个公共本体模型并将结构化内容表示为语义三元组，可以合并大量的结构化和非结构化数据。 Repubba提出了新的方法，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）接口。为了使QA鲁棒，提出了一种创新的混合方法，从RDF存储以及直接从索引文本文档中得出答案。交付在商业级RDF存储上运行的问答系统的潜在影响是显著的，因为它满足了该存储的用户的需求，以方便地访问更多信息，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。该提案还将使技术软件能够推进语义网。 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。

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.