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Tooling Framework for Instantiating Natural Language Querying System

机译:实例化自然语言查询系统的工具框架

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Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain.
机译:近年来,自然语言查询(NLQ)接口从诸如知识库之类的结构化数据源中检索信息的需求日益增长。使用此界面,业务用户可以直接与数据库进行交互,而无需了解查询语言或数据模式。我们之前的工作描述了一种称为ATHENA的自然语言查询引擎,该引擎在易用性以及与数据存储,格式和流的兼容性方面存在多个缺点。在此演示文件中,我们提供了一种工具框架来应对这些挑战,以便人们可以最轻松地实例化NLQ系统。我们的框架可以轻松,实用地应用于所有NLIDB场景,这些场景涉及结构化数据,文件格式和本体的不同来源,从而以最少的人工配置就可以在它们之上进行自然语言查询。我们介绍了构建此类系统所面临的挑战的工具设计和解决方案,并展示了其在医疗领域的适用性。

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