Anu question answering system
Balaji Ganesan, Avirup Saha, et al.
ISWC-Posters 2020
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 ows. 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.
Balaji Ganesan, Avirup Saha, et al.
ISWC-Posters 2020
Rajeev Gupta, Shourya Roy, et al.
ICAC 2006
Elliot Linzer, M. Vetterli
Computing
David S. Kung
DAC 1998