Description Logic Reasoning with Syntactic Updates

Halashek-Wiener, Christian; Parsia, Bijan; Sirin, Evren

doi:10.21236/ada455672

Cited by 21 publications

(28 citation statements)

References 8 publications

(24 reference statements)

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“…In order to serve as an efficient reasoning component within a realistic setting it is necessary to perform well under small ABox updates. First results in this research direction, e. g. [7], need to be evaluated by appropriate benchmarks.…”

Section: On Benchmarking Owl Reasonersmentioning

confidence: 99%

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Real-World Reasoning with OWL

Weithöner

Liebig

Luther

et al. 2007

Lecture Notes in Computer Science

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Abstract. This work is motivated by experiences in the course of developing an ontology-based application within a real-world setting. We found out that current benchmarks are not well suited to provide helpful hints for users who seek for an appropriate reasoning system able to deal with expressive terminological descriptions, large volumes of assertional data, and frequent updates in a sound and complete way. This paper tries to provide some insights into currently available reasoning approaches and aims at identifying requirements to make future benchmarks more useful for application developers. On Benchmarking OWL ReasonersHaving sufficiently exhaustive knowledge about the influence of the underlying reasoning approach on the practical tractability of a particular ontology is of fundamental importance when selecting an inference engine for a real-world application. By real-world we mean an ontology-based application with an expressivity at least beyond ALC, containing more than thousands of individuals, and an inference response time of less than a second, even in a dynamical setting of frequent ontology updates. For instance, context-aware applications want to offer services to users based on their actual situation. Experiences in the course of operating a context-aware application for mobile users [1] clearly have shown that the quality of such an application hosted on a server significantly depends on the availability of reliable and scalable reasoning systems able to deal with constantly changing data. In order to meet real-world needs a reasoning system also has to offer a sufficiently expressive query language as well as a flexible and efficient communication interface.Unfortunately, current benchmarks or system comparisons neither draw a clear picture of the landscape of practically tractable language fragments with respect to large amounts of instance data, give valuable insights into pros and cons of different reasoning approaches, identify performance penalties caused by certain language features, nor consider issues such as updates, incremental query answering, or interfaces.For instance, many benchmarks consist of synthetical generated and sparsely interrelated data using inexpressive ontology languages such as the widely used

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Section: On Benchmarking Owl Reasonersmentioning

confidence: 99%

“…Initial empirical results from [7], performed with an experimental version of Pellet, indicate that such algorithms for SHOIN (D) can be quite effective.…”

Section: Dynamic Behaviormentioning

confidence: 99%

Real-World Reasoning with OWL

Weithöner

Liebig

Luther

et al. 2007

Lecture Notes in Computer Science

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“…Notable exceptions include [18][19][20]31]; these papers, however, investigate the problem of incremental reasoning using model-caching techniques in application scenarios that involve changes only in the ABox.…”

Section: Related Workmentioning

confidence: 99%

Incremental Classification of Description Logics Ontologies

Grau

Halaschek-Wiener²,

Kazakov³

et al. 2010

J Autom Reasoning

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The development of ontologies involves continuous but relatively small modifications. However, existing ontology reasoners do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a collection of techniques for incremental reasoning -that is, reasoning that reuses information obtained from previous versions of an ontology. We have applied our results to incremental classification of OWL ontologies and found significant improvement over regular classification time on a set of real-world ontologies.

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“…NCI 6 , and the Gene Ontology 7 are expressed in a simple fragment of OWL DL. In contrast, GALEN 8 , and NASA's SWEET ontology 9 are written in a more expressive language. Table 5 includes their expressivity, number of atomic concepts and axioms, total classification time in Pellet, and the percentage of possible subsumption relations that actually hold between atomic concepts.…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…Notable exceptions include [7,8,9,12]; these papers, however, investigate the problem of incremental reasoning using model-caching techniques in application scenarios that involve changes only in the ABox.…”

Section: Related Workmentioning

confidence: 99%

History Matters: Incremental Ontology Reasoning Using Modules

2007

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Abstract. The development of ontologies involves continuous but relatively small modifications. Existing ontology reasoners, however, do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a technique for incremental reasoning-that is, reasoning that reuses information obtained from previous versions of an ontology-based on the notion of a module. Our technique does not depend on a particular reasoning calculus and thus can be used in combination with any reasoner. We have applied our results to incremental classification of OWL DL ontologies and found significant improvement over regular classification time on a set of real-world ontologies.

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Description Logic Reasoning with Syntactic Updates

Cited by 21 publications

References 8 publications

Real-World Reasoning with OWL

Real-World Reasoning with OWL

Incremental Classification of Description Logics Ontologies

History Matters: Incremental Ontology Reasoning Using Modules

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