Q²Forge Minting Competency Questions and SPARQL Queries for Question-Answering Over Knowledge Graphs

Q²Forge: Minting Competency Questions and SPARQL Queries for
Question-Answering Over Knowledge Graphs - by Franck Michel

Abstract: Training and testing language models to produce high quality
SPARQL queries from natural language questions requires substantial
datasets of question-query pairs. Q²Forge addresses the challenge of
generating new competency questions for a Knowledge Graph (KG) and
corresponding SPARQL queries. It iteratively validates those queries
with human feedback and LLM as a judge. Q²Forge is open source, generic,
extensible and modular, meaning that the different modules of the
application (CQ generation, query generation and query refinement) can
be used separately, as an integrated pipeline, or replaced by
alternative services. The result is a complete pipeline from competency
question formulation to query evaluation, supporting the creation of
reference question-query sets for any target KG.

This work was supported by the French government through the France 2030
investment plan managed by the National Research Agency (ANR), as part
of the Initiative of Excellence Université Côte d’Azur (ANR-15-IDEX-01).
Additional support came from French Government’s France 2030 investment
plan (ANR-22-CPJ2-0048-01), through 3IA Cote d’Azur (ANR-23-IACL-0001)
as well as the MetaboLinkAI bilateral project (ANR-24-CE93-0012-01 and
SNSF 10002786).

Speaker: Franck MICHEL, Université Côte d'Azur, CNRS, Inria.
https://w3id.org/people/franckmichel

Publication: https://hal.science/hal-05070442