Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review

TL;DR

This systematic review highlights the significant reproducibility challenges in domain-specific knowledge graphs within the life sciences, revealing that only a tiny fraction are reproducible, underscoring the need for improved practices.

Contribution

The paper provides a comprehensive analysis of reproducibility in domain-specific KGs, comparing 250 KGs across 19 domains and identifying critical gaps and challenges.

Findings

Only 3.2% of KGs provide publicly available source code.

Just one system out of eight reproducible KGs passed the assessment.

Reproducible KGs constitute only 0.4% of published domain-specific KGs.

Abstract

Knowledge graphs (KGs) are widely used for representing and organizing structured knowledge in diverse domains. However, the creation and upkeep of KGs pose substantial challenges. Developing a KG demands extensive expertise in data modeling, ontology design, and data curation. Furthermore, KGs are dynamic, requiring continuous updates and quality control to ensure accuracy and relevance. These intricacies contribute to the considerable effort required for their development and maintenance. One critical dimension of KGs that warrants attention is reproducibility. The ability to replicate and validate KGs is fundamental for ensuring the trustworthiness and sustainability of the knowledge they represent. Reproducible KGs not only support open science by allowing others to build upon existing knowledge but also enhance transparency and reliability in disseminating information. Despite the growing number of domain-specific KGs, a comprehensive analysis concerning their reproducibility has been lacking. This paper addresses this gap by offering a general overview of domain-specific KGs and comparing them based on various reproducibility criteria. Our study over 19 different domains shows only eight out of 250 domain-specific KGs (3.2%) provide publicly available source code. Among these, only one system could successfully pass our reproducibility assessment (14.3%). These findings highlight the challenges and gaps in achieving reproducibility across domain-specific KGs. Our finding that only 0.4% of published domain-specific KGs are reproducible shows a clear need for further research and a shift in cultural practices.