Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction

Viachaslau Sazonau; Ulrike Sattler

doi:10.1007/978-3-319-68288-4_34

Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction

Viachaslau Sazonau, Ulrike Sattler

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Abstract

Automated acquisition (learning) of ontologies from data has attracted research interest because it can complement manual, expensive construction of ontologies. We investigate the problem of General Terminology Induction in OWL, i.e. acquiring general, expressive TBox axioms (hypotheses) from an ABox (data). We define novel measures designed to rigorously evaluate the quality of hypotheses while respecting the standard semantics of OWL. We propose an informed, data-driven algorithm that constructs class expressions for hypotheses in OWL and guarantees completeness. We empirically evaluate the quality measures on two corpora of ontologies and run a case study with a domain expert to gain insight into applicability of the measures and acquired hypotheses. The results show that the measures capture different quality aspects and not only correct hypotheses can be interesting.

Original language	English
Title of host publication	Proceedings of The 16th International Semantic Web Conference (ISWC 2017)
Pages	577-593
DOIs	https://doi.org/10.1007/978-3-319-68288-4_34
Publication status	Published - 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-319-68288-4_34

SazonauISWC17Accepted author manuscript, 303 KB

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title = "Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction",

abstract = "Automated acquisition (learning) of ontologies from data has attracted research interest because it can complement manual, expensive construction of ontologies. We investigate the problem of General Terminology Induction in OWL, i.e. acquiring general, expressive TBox axioms (hypotheses) from an ABox (data). We define novel measures designed to rigorously evaluate the quality of hypotheses while respecting the standard semantics of OWL. We propose an informed, data-driven algorithm that constructs class expressions for hypotheses in OWL and guarantees completeness. We empirically evaluate the quality measures on two corpora of ontologies and run a case study with a domain expert to gain insight into applicability of the measures and acquired hypotheses. The results show that the measures capture different quality aspects and not only correct hypotheses can be interesting.",

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AU - Sazonau, Viachaslau

AU - Sattler, Ulrike

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N2 - Automated acquisition (learning) of ontologies from data has attracted research interest because it can complement manual, expensive construction of ontologies. We investigate the problem of General Terminology Induction in OWL, i.e. acquiring general, expressive TBox axioms (hypotheses) from an ABox (data). We define novel measures designed to rigorously evaluate the quality of hypotheses while respecting the standard semantics of OWL. We propose an informed, data-driven algorithm that constructs class expressions for hypotheses in OWL and guarantees completeness. We empirically evaluate the quality measures on two corpora of ontologies and run a case study with a domain expert to gain insight into applicability of the measures and acquired hypotheses. The results show that the measures capture different quality aspects and not only correct hypotheses can be interesting.

AB - Automated acquisition (learning) of ontologies from data has attracted research interest because it can complement manual, expensive construction of ontologies. We investigate the problem of General Terminology Induction in OWL, i.e. acquiring general, expressive TBox axioms (hypotheses) from an ABox (data). We define novel measures designed to rigorously evaluate the quality of hypotheses while respecting the standard semantics of OWL. We propose an informed, data-driven algorithm that constructs class expressions for hypotheses in OWL and guarantees completeness. We empirically evaluate the quality measures on two corpora of ontologies and run a case study with a domain expert to gain insight into applicability of the measures and acquired hypotheses. The results show that the measures capture different quality aspects and not only correct hypotheses can be interesting.

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DO - 10.1007/978-3-319-68288-4_34

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T3 - Lecture Notes in Computer Science

SP - 577

EP - 593

BT - Proceedings of The 16th International Semantic Web Conference (ISWC 2017)

ER -

Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction

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