A Refinement Operator for Description Logics

Badea, Liviu; Nienhuys-Cheng, Shan-Hwei

doi:10.1007/3-540-44960-4_3

Cited by 71 publications

(70 citation statements)

References 12 publications

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“…Note that this has been explained in [6] already, but we repeat the argument here. Consider the following knowledge base containing a person a with two male children a 1 and a 2 :…”

Section: Reduction Of Instance Checkssupporting

confidence: 61%

“…Accepting such a suggestion can still be a good choice, because the problem often lies elsewhere in the knowledge base, but was not obvious before, since the ontology was not sufficiently expressive for reasoners to detect it. This is illustrated by a screencast available from the plugin homepage 6 , where the ontology becomes inconsistent after adding the axiom, and the real source of the problem is fixed afterwards. Being able to make such suggestions can be seen as a strength of the plugin.…”

Section: The Protégé Pluginmentioning

confidence: 99%

“…[8,9], which used so-called least common subsumers to solve the learning problem (a modified variant of the problem defined in this article). Later, [6] invented a refinement operator for ALER and proposed to solve the problem by using a top-down approach. [13,18,19] combine both techniques and implement them in the YINYANG tool.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Class expression learning for ontology engineering

Lehmann

Auer

Bühmann

et al. 2011

Journal of Web Semantics

102

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“…Note that this has been explained in [6] already, but we repeat the argument here. Consider the following knowledge base containing a person a with two male children a 1 and a 2 :…”

Section: Reduction Of Instance Checkssupporting

confidence: 61%

Section: The Protégé Pluginmentioning

confidence: 99%

See 1 more Smart Citation

Class expression learning for ontology engineering

Lehmann

Auer

Bühmann

et al. 2011

Journal of Web Semantics

102

View full text Add to dashboard Cite

“…[3], which used so-called least common subsumers to solve the learning problem (a modified variant of the problem defined in this article). Later, [2] invented a refinement operator for ALER and proposed to solve the problem by using a top-down approach. [4,13] combine both techniques and implement them in the YINYANG tool.…”

Section: Related Workmentioning

confidence: 99%

ORE - A Tool for Repairing and Enriching Knowledge Bases

Lehmann

Bühmann

2010

Lecture Notes in Computer Science

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Abstract. While the number and size of Semantic Web knowledge bases increases, their maintenance and quality assurance are still difficult. In this article, we present ORE, a tool for repairing and enriching OWL ontologies. State-ofthe-art methods in ontology debugging and supervised machine learning form the basis of ORE and are adapted or extended so as to work well in practice. ORE supports the detection of a variety of ontology modelling problems and guides the user through the process of resolving them. Furthermore, the tool allows to extend an ontology through (semi-)automatic supervised learning. A wizard-like process helps the user to resolve potential issues after axioms are added.

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“…A weaker result was claimed to hold, but not proved, in [4]. We formulate our result for the description logic AL, the concepts of which are inductively defined as follows: , ⊥, ∃r.…”

Section: Analysing the Properties Of Refinement Operatorsmentioning

confidence: 93%

Foundations of Refinement Operators for Description Logics

Lehmann

Hitzler

Inductive Logic Programming

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In order to leverage techniques from Inductive Logic Programming for the learning in description logics (DLs), which are the foundation of ontology languages in the Semantic Web, it is important to acquire a thorough understanding of the theoretical potential and limitations of using refinement operators within the description logic paradigm. In this paper, we present a comprehensive study which analyses desirable properties such operators should have. In particular, we show that ideal refinement operators in general do not exist, which is indicative of the hardness inherent in learning in DLs. We also show which combinations of desirable properties are theoretically possible, thus providing an important step towards the definition of practically applicable operators.

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A Refinement Operator for Description Logics

Cited by 71 publications

References 12 publications

Class expression learning for ontology engineering

Class expression learning for ontology engineering

ORE - A Tool for Repairing and Enriching Knowledge Bases

Foundations of Refinement Operators for Description Logics

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