Knowledge Rich Natural Language Queries over Structured Biological Databases

Abstract

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made.

Parsing Natural Language Queries for Extracting Data from Large-Scale Geospatial Transportation Asset Repositories

“…In the last decade, a plethora of research have been conducted to developed natural language query systems for various different query languages including SQL (Popescu et al 2003, Saha et al 2016, XQuery (Li et al 2006, Li et al 2007a, Li et al 2007b, and Sparql (Zou et al 2014, Dubey et al 2016). These systems have been implemented for different types of data such as bibliographic information (Zhu et al 2016), spatial analysis of crimes (Zhang et al 2009), biological data (Jamil 2017), and geospatial maps (Cai et al 2005, Du et al 2005, Lawrence et al 2016, Haas et al 2016). The communication between users and computers varies among NLIDB systems.…”