Abstract. We propose a novel approach for performance prediction of OWL reasoners. It selects suitable, small ontology subsets, and then extrapolates rea-soner’s performance on them to the whole ontology. We investigate intercorrela-tion of ontology features using PCA. Finally, we discuss various error measures for performance prediction and compare our approach against an existing one using these measures.
This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that suc...
In this article the authors discuss the challenges of performing reasoning on large scale RDF datase...
Materialized knowledge bases perform inferencing when data is loaded into them, so that answering qu...
We propose a novel approach to performance prediction of OWL reasoners that selects suitable, small ...
For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive ta...
Abstract. A key issue in semantic reasoning is the computational com-plexity of inference tasks on e...
In this article, the authors introduce the notion of ABox intensity in the context of predicting rea...
International audienceWe address the problem of predicting a score for candidate axioms within the c...
Ontology classification - the computation of subsumption hierarchies for classes and properties - is...
Abstract. Predicting the performance of a tableau reasoner for an OWL ontology is generally hard. It...
Within the context of ontology learning, we consider the problem of selecting candidate axioms throu...
This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that suc...
AbstractSeveral techniques and applications have been proposed to aid the decision taking process in...
International audienceCandidate axiom scoring is the task of assessing the acceptability of a candid...
HermiT is the only reasoner we know of that fully supports the OWL 2 standard, and that correctly re...
This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that suc...
In this article the authors discuss the challenges of performing reasoning on large scale RDF datase...
Materialized knowledge bases perform inferencing when data is loaded into them, so that answering qu...
We propose a novel approach to performance prediction of OWL reasoners that selects suitable, small ...
For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive ta...
Abstract. A key issue in semantic reasoning is the computational com-plexity of inference tasks on e...
In this article, the authors introduce the notion of ABox intensity in the context of predicting rea...
International audienceWe address the problem of predicting a score for candidate axioms within the c...
Ontology classification - the computation of subsumption hierarchies for classes and properties - is...
Abstract. Predicting the performance of a tableau reasoner for an OWL ontology is generally hard. It...
Within the context of ontology learning, we consider the problem of selecting candidate axioms throu...
This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that suc...
AbstractSeveral techniques and applications have been proposed to aid the decision taking process in...
International audienceCandidate axiom scoring is the task of assessing the acceptability of a candid...
HermiT is the only reasoner we know of that fully supports the OWL 2 standard, and that correctly re...
This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that suc...
In this article the authors discuss the challenges of performing reasoning on large scale RDF datase...
Materialized knowledge bases perform inferencing when data is loaded into them, so that answering qu...