A large collection of bioinformatics question-query pairs over federated knowledge graphs: methodology and applications
Journal:
arXiv
Published Date:
Oct 8, 2024
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.
