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Knowledge Open Network Queries for Research (KONQUER)

Program Description

Technological advancements have allowed researchers to gain access to large volumes of data, but it can be difficult to analyze and locate specific information when there are numerous databases to search through. Imagine having the tool to answer scientific questions that require information from various fields by using a search engine that gathers factual data from medical and environmental databases, climate models, research papers, health reports, maps, and so forth. 

This project aims to solve this challenge by developing a search engine, called KONQUER (Knowledge Open Network QUEries for Research). KONQUER allows researchers to search for relevant data sets across multiple, scientific domains, thus enabling them to conduct more accurate studies that address national concerns and reuse data that the public has already funded. A user would be able to type in a question in KONQUER and natural language processing would decompose the question to retrieve information from the relevant data sources.

The major goals of the KONQUER project are to:

  1. Extend the metadata model for geo/climate data sets
  2. Semi-automate metadata ingestion pipelines
  3. Develop a prototype data search engine that can query multiple data sources for selected use cases

Principal Investigators (PIs):  Lucila Ohno-Machado (UCSD), Peter Rose (UCSD), Ilya Zaslavsky (UCSD), Hua Xu (UTH), Joseph Hamman (NCAR)

Grant: NSF Grant 937136

Contact Email:

Start Date: September 2019

End Date: May 2021