KGEV

KGEV enables exploration, visualization, and interactive analysis of biomedical knowledge graphs to support structured navigation of literature-derived relationships and hypothesis generation.

Key Features:

• Five-stage Construction Process: Constructs knowledge graphs via a five-stage pipeline: Triple Extraction, Triple Filtration, Metadata Preparation, Knowledge Integration, and Graph Database Preparation.
• Graph querying and analysis tools: Provides node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest-path calculation for graph interrogation.
• Fast retrieval of supporting texts: Enables rapid access to source texts that support graph relationships for human review and validation.

Scientific Applications:

• COVID-19 knowledge graph construction and navigation: Used to generate a comprehensive knowledge graph incorporating heterogeneous information from the CORD-19 dataset and eight other data repositories to facilitate structured navigation of COVID-19 literature.
• Phenotype and disease analysis with HPO: Applied to study clinical phenotypic presentations using the Human Phenotype Ontology (HPO) and to explore gene-phenotype-disease-drug relationships interactively.

Methodology:

KGEV queries a backend graph database to support efficient data handling and retrieval and integrates and visualizes heterogeneous datasets.