A large collection of bioinformatics question-query pairs over federated knowledge graphs: methodology and applications

TL;DR

This paper presents a large, standardized collection of natural language questions and SPARQL queries over bioinformatics knowledge graphs, along with tools for visualization and query editing, to facilitate data retrieval and machine learning.

Contribution

It introduces a comprehensive dataset of over 1000 question-query pairs, a unified representation methodology, and open-source applications for knowledge graph querying in bioinformatics.

Findings

Collection includes 1000+ question-query pairs, with 65 federated queries.

Proposes a standardized, minimal-metadata representation methodology.

Provides reusable tools like query visualizations and editors.

Abstract

Background. In the last decades, several life science resources have structured data using the same framework and made these accessible using the same query language to facilitate interoperability. Knowledge graphs have seen increased adoption in bioinformatics due to their advantages for representing data in a generic graph format. For example, yummydata.org catalogs more than 60 knowledge graphs accessible through SPARQL, a technical query language. Although SPARQL allows powerful, expressive queries, even across physically distributed knowledge graphs, formulating such queries is a challenge for most users. Therefore, to guide users in retrieving the relevant data, many of these resources provide representative examples. These examples can also be an important source of information for machine learning, if a sufficiently large number of examples are provided and published in a common, machine-readable and standardized format across different resources. Findings. We introduce a large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1000 example questions and queries, including 65 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology. Conclusions. We encourage the community to adopt and extend the proposed methodology, towards richer KG metadata and improved Semantic Web services.