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

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，即如何将其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、大众分类等)相链接。如果没有专家花费大量时间和精力在系统和数据模型定制上，就没有简单的方法来执行联合搜索，更不用说在如此多样化的数据源上启用更智能的应用程序了。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模式定义公共本体模型并将结构化内容表示为语义三元组，将大量结构化和非结构化数据合并成为可能。Lymba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，并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更可靠，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获取答案。交付在商业级RDF商店运行的问答系统的潜在影响是显著的，因为它满足了该商店的用户轻松访问更多信息并快速实施以自然语言问题为主要工具的智能应用程序的需求。该提议还导致了使技术软件能够推动语义网的发展。如果部署成功，拟议中的研究有可能转化为可行的商业产品，带来可观的收入。

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.