BigCQ: A large-scale synthetic dataset of competency question patterns formalized into SPARQL-OWL query templates

• URL: http://arxiv.org/abs/2105.09574v1
• Date: Thu, 20 May 2021 07:59:59 GMT
• Title: BigCQ: A large-scale synthetic dataset of competency question patterns formalized into SPARQL-OWL query templates
• Authors: Dawid Wi\'sniewski and J\k{e}drzej Potoniec and Agnieszka {\L}awrynowicz
• Abstract summary: BigCQ is the biggest dataset of CQ templates with their formalizations into SPARQL-OWL query templates. We describe the dataset in detail, provide a description of the process leading to the creation of the dataset and analyze how well the dataset covers real-world examples.
• Score: 0.06445605125467574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Competency Questions (CQs) are used in many ontology engineering methodologies to collect requirements and track the completeness and correctness of an ontology being constructed. Although they are frequently suggested by ontology engineering methodologies, the publicly available datasets of CQs and their formalizations in ontology query languages are very scarce. Since first efforts to automate processes utilizing CQs are being made, it is of high importance to provide large and diverse datasets to fuel these solutions. In this paper, we present BigCQ, the biggest dataset of CQ templates with their formalizations into SPARQL-OWL query templates. BigCQ is created automatically from a dataset of frequently used axiom shapes. These pairs of CQ templates and query templates can be then materialized as actual CQs and SPARQL-OWL queries if filled with resource labels and IRIs from a given ontology. We describe the dataset in detail, provide a description of the process leading to the creation of the dataset and analyze how well the dataset covers real-world examples. We also publish the dataset as well as scripts transforming axiom shapes into pairs of CQ patterns and SPARQL-OWL templates, to make engineers able to adapt the process to their particular needs.

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