Automatic Translation of Competency Questions into SPARQL-OWL Queries

Wiśniewski, Dawid

doi:10.1145/3184558.3186575

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…They were used to check how CQ patterns map to SPARQL-OWL query constructs. Wisniewski et al [25] proposed an approach to use such patterns in the process of automatic translation of CQs into ontology query language, in order to automatically assess the completeness of a given ontology.…”

Section: Related Workmentioning

confidence: 99%

ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

Wiśniewski

Potoniec

Ławrynowicz

2022

Foundations of Computing and Decision Sciences

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Glossary of Terms extraction from textual requirements is an important step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human interpretable, rule-based method named ReqTagger, which can extract candidates for ontology entities (classes or instances) and relations (data or object properties) from textual requirements automatically. We compare ReqTagger to existing automatic methods on an evaluation benchmark consisting of over 550 requirements and tagged with over 1700 entities and relations expected to be extracted. We discuss the quality of ReqTagger and provide details showing why it outperforms other methods. We also publish both the evaluation dataset and the implementation of ReqTagger.

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Section: Related Workmentioning

confidence: 99%

ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

Wiśniewski

Potoniec

Ławrynowicz

2022

Foundations of Computing and Decision Sciences

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“…For example, [12] is a Web application designed for providing the XD methodology with a testing toolbox based on the representation of CQs to SPARQL queries. In this context [13] provides a solution for generating SPARQL queries from CQs. Our solution is meant to be used as a component of the aforementioned agile methodologies.…”

Section: Related Workmentioning

confidence: 99%

Automatically Drafting Ontologies from Competency Questions with FrODO

Gangemi

Lippolis

Lodi

et al. 2022

Towards a Knowledge-Aware AI

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We present the Frame-based ontology Design Outlet (FrODO), a novel method and tool for drafting ontologies from competency questions automatically. Competency questions are expressed as natural language and are a common solution for representing requirements in a number of agile ontology engineering methodologies, such as the eXtreme Design (XD) or SAMOD. FrODO builds on top of FRED. In fact, it leverages the frame semantics for drawing domain-relevant boundaries around the RDF produced by FRED from a competency question, thus drafting domain ontologies. We carried out a user-based study for assessing FrODO in supporting engineers for ontology design tasks. The study shows that FrODO is effective in this and the resulting ontology drafts are qualitative.

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“…To automate the process of vocabulary extraction from CQs, Wisniewski et al [22] proposed a machine learning-based tagger, which automatically detects candidates for classes and properties to be modelled in an ontology. Wisniewski also proposed the idea of automatic translation of CQs into SPARQL-OWL queries [20], so that having an ontology under development, a set of CQs can be used to evaluate the quality of the ontology without the need of manual translation of CQs into query formalization.…”

Section: Ontology Requirements and Their Analysismentioning

confidence: 99%

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

Wiśniewski,

Potoniec,

Ławrynowicz

2021

Preprint

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Competency Questions (CQs) are used in many ontology engineering methodologies to collect requirements and track 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 details, provide a description of the process leading to 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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Automatic Translation of Competency Questions into SPARQL-OWL Queries

Cited by 7 publications

References 17 publications

ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

Automatically Drafting Ontologies from Competency Questions with FrODO

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

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