Data Exploration and Validation on dense knowledge graphs for biomedical research

TL;DR

This paper introduces a comprehensive method for exploring and validating dense biomedical knowledge graphs, integrating text mining, ontologies, and graph theory to enhance data quality, hypothesis validation, and semantic analysis.

Contribution

It presents a novel approach combining graph theory and linked data to improve biomedical knowledge graph analysis and validation.

Findings

Extended biomedical knowledge graph with context data

Applied graph theory for quality control and hypothesis validation

Demonstrated potential for semantic question answering using FAIR principles

Abstract

Here we present a holistic approach for data exploration on dense knowledge graphs as a novel approach with a proof-of-concept in biomedical research. Knowledge graphs are increasingly becoming a vital factor in knowledge mining and discovery as they connect data using technologies from the semantic web. In this paper we extend a basic knowledge graph extracted from biomedical literature by context data like named entities and relations obtained by text mining and other linked data sources like ontologies and databases. We will present an overview about this novel network. The aim of this work was to extend this current knowledge with approaches from graph theory. This method will build the foundation for quality control, validation of hypothesis, detection of missing data and time series analysis of biomedical knowledge in general. In this context we tried to apply multiple-valued decision diagrams to these questions. In addition this knowledge representation of linked data can be used as FAIR approach to answer semantic questions. This paper sheds new lights on dense and very large knowledge graphs and the importance of a graph-theoretic understanding of these networks.