Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies

Gonçalves, Rafael S.; Parsia, Bijan; Sattler, Ulrike

doi:10.1007/978-3-642-35176-1_6

Cited by 20 publications

(29 citation statements)

References 14 publications

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“…In [6], it is introduced that reasoning tasks on ontologies constructed from an expressive description logic have a high worst case complexity, by analyzing experimental results that divided each of several ontologies into 4 and 8 random subsets of equal size and measured classification times of these subsets as increments. They reported that some ontologies exhibit non-linear sensitivity on their inference performance.…”

Section: Query and Inference Performance Predictionmentioning

confidence: 99%

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Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints

Yamada

Yamagata

Fukuta

2017

IEICE Trans. Inf. & Syst.

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SUMMARYOn an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.

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Section: Query and Inference Performance Predictionmentioning

confidence: 99%

“…These techniques are important to make intelligent agents accessible to data on its external world. Techniques to utilize reasoning capability based on ontology have been developed to overcome several issues, such as higher complexity in worst case [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints

Yamada

Yamagata

Fukuta

2017

IEICE Trans. Inf. & Syst.

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“…Clause (26) says that each element in I corresponding to a predecessor of an element represented by v 2 must satisfy F or G; hence, we apply the Pred rule to edge (17) and clauses (15) and (26) to derive clause (28). Furthermore, we also apply the Pred rule to edge (27) and thus derive clause (29).…”

Section: Intuitionsmentioning

confidence: 99%

“…Clause (15) says that the elements in I corresponding to v 1 must have a successor in which B holds. To satisfy this requirement, we use the Succ rule to introduce context v 2 and add the edge (17) from v 1 to v 2 ; the edge is labeled with the concept ∃R.B that it satisfies.…”

Section: Intuitionsmentioning

confidence: 99%

“…The performance of such reasoners is, however, relatively brittle, and there exist ontologies (for example the GALEN ontology 1 ) that none of these reasoners is able to process. Although there has been some work on identifying the qualitative features of an ontology that may degrade the performance of a given reasoner [15], no existing method can provide a quantitative measure of the "difficulty" of reasoning with a particular ontology.…”

Section: Introductionmentioning

confidence: 99%

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Consequence-based and fixed-parameter tractable reasoning in description logics

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Motik

Horrocks

2014

Artificial Intelligence

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In this paper we investigate the consequence-based algorithms that are nowadays commonly used for subsumption reasoning with description logic ontologies, presenting the following novel results. First, we present a very general consequence-based reasoning algorithm that can be instantiated so as to capture the essential features of the known algorithms. Second, we use this algorithm to develop a novel framework for a quantitative and parametric analysis of the complexity of subsumption reasoning in description logic ontologies. Our approach is based on the notion of a decomposition-a graph-like structure that, roughly speaking, summarizes the models relevant during ontology reasoning. We identify width and length as decomposition parameters that determine the "level" of combinatorial reasoning. We prove that, when parameterized by a decomposition, our consequencebased algorithm runs in time that is fixed-parameter tractable in width and length. Third, we briefly discuss how length and width characterize the behavior of tableau algorithms. Fourth, we show that the width and length of existing ontologies are reasonably small, which, we believe, explains the good performance of consequence-based algorithms in practice. We thus obtain a sound foundation for a practical implementation of ontology reasoners, as well as a way to analyze the reasoners' performance.

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