Generating Expressive Correspondences: An Approach Based on User Knowledge Needs and A-Box Relation Discovery

Thiéblin, Elodie; Haemmerlé, Ollivier; Santos, Cássia Trojahn dos

doi:10.1007/978-3-030-62419-4_32

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Evaluation datasets An automatic evaluation was performed on the populated version of the OAEI Conference benchmark [32]. This dataset is composed of 5 ontologies, with 100 manually generated CQAs.…”

Section: Evaluation Settingsmentioning

confidence: 99%

“…); (iii) evaluating the approach on both synthetic and real-world benchmarks; and (iv) comparing the proposed approach to state-of-the-art systems. The paper extends the work in [29] in several directions: (i) providing a deeper description of the steps of the approach; (ii) discussing the impact of the different design and implementation choices; (iii) extending the comparison of the approach to systems participating in the most recent OAEI campaigns; and (iv) presenting an optimized version of the system that improves runtime. The source code is available 2 under the GNU Lesser General Public License v2.1.…”

Section: Introductionmentioning

confidence: 95%

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CANARD: An approach for generating expressive correspondences based on competency questions for alignment

Thiéblin,

Sousa,

Haemmerlé

et al. 2024

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Ontology matching aims at making ontologies interoperable. While the field has fully developed in the last years, most approaches are still limited to the generation of simple correspondences. More expressiveness is, however, required to better address the different kinds of ontology heterogeneities. This paper presents CANARD (Complex Alignment Need and A-box based Relation Discovery), an approach for generating expressive correspondences that rely on the notion of competency questions for alignment (CQA). A CQA expresses the user knowledge needs in terms of alignment and aims at reducing the alignment space. The approach takes as input a set of CQAs as SPARQL queries over the source ontology. The generation of correspondences is performed by matching the subgraph from the source CQA to the similar surroundings of the instances from the target ontology. Evaluation is carried out on both synthetic and real-world datasets. The impact of several approach parameters is discussed. Experiments have showed that CANARD performs, overall, better on CQA coverage than precision and that using existing same:As links, between the instances of the source and target ontologies, gives better results than exact label matches of their labels. The use of CQA improved also both CQA coverage and precision with respect to using automatically generated queries. The reassessment of the counter-example increased significantly the precision, to the detriment of runtime. Finally, experiments on large datasets showed that CANARD is one of the few systems that can perform on large knowledge bases, but depends on regularly populated knowledge bases and the quality of instance links.

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Section: Evaluation Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

CANARD: An approach for generating expressive correspondences based on competency questions for alignment

Thiéblin,

Sousa,

Haemmerlé

et al. 2024

Self Cite

View full text Add to dashboard Cite

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Automatic evaluation of complex alignments: An instance-based approach

Thiéblin

Haemmerlé

Santos

2021

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Ontology matching is the task of generating a set of correspondences (i.e., an alignment) between the entities of different ontologies. While most efforts on alignment evaluation have been dedicated to the evaluation of simple alignments (i.e., those linking one single entity of a source ontology to one single entity of a target ontology), the emergence of matchers providing complex alignments (i.e., those composed of correspondences involving logical constructors or transformation functions) requires new strategies for addressing the problem of automatically evaluating complex alignments. This paper proposes (i) a benchmark for complex alignment evaluation composed of an automatic evaluation system that relies on queries and instances, and (ii) a dataset about conference organisation. This dataset is composed of populated ontologies and a set of competency questions for alignment as SPARQL queries. State-of-the-art alignments are evaluated and a discussion on the difficulties of the evaluation task is provided.

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Generating Expressive Correspondences: An Approach Based on User Knowledge Needs and A-Box Relation Discovery

Cited by 2 publications

References 22 publications

CANARD: An approach for generating expressive correspondences based on competency questions for alignment

CANARD: An approach for generating expressive correspondences based on competency questions for alignment

Automatic evaluation of complex alignments: An instance-based approach

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