Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe

TL;DR

This paper presents a hybrid methodology that extracts metadata and uses domain knowledge to create knowledge graphs from complex datasets, improving data discovery and enabling complex queries in natural hazard engineering data.

Contribution

The paper introduces a novel hybrid approach combining metadata extraction and domain knowledge to construct knowledge graphs from unstructured scientific data.

Findings

Effective knowledge graph construction from natural hazard datasets

Enhanced ability to perform complex queries on the data

Potential to transform data-driven scientific discoveries

Abstract

Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata and leverages scientific domain knowledge to synthesize a new dataset from the original to construct knowledge graphs. We demonstrate our approach's effectiveness through a case study on the natural hazard engineering dataset on ``LEAP Liquefaction'' hosted on DesignSafe. Traditional lexical search on DesignSafe is limited in uncovering hidden relationships within the data. Our knowledge graph enables complex queries and fosters new scientific insights by accurately identifying relevant entities and establishing their relationships within the dataset. This innovative implementation can transform the landscape of data-driven discoveries across various scientific domains.