OWL2Bench: A Benchmark for OWL 2 Reasoners

Singh, Gunjan; Bhatia, Sumit; Mutharaju, Raghava

doi:10.1007/978-3-030-62466-8_6

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…1,104 in a single file). The more recent OWL2Bench dataset [11], proposed in 2020, contains exactly one owl:hasValue restriction, mainly for checking whether or not a reasoner supports that construct, but does not allow for evaluating that support at scale. In contrast, the CaLiGraph ontology makes much heavier use of those constructs, as shown above, and therefore imposes more challenging scalability requirements with respect to that reasoning task.…”

Section: Given the T-box Above The Derived Axioms According To The De...mentioning

confidence: 99%

The CaLiGraph Ontology as a Challenge for OWL Reasoners

Heist¹,

Paulheim²

2021

Preprint

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CaLiGraph is a large-scale cross-domain knowledge graph generated from Wikipedia by exploiting the category system, list pages, and other list structures in Wikipedia, containing more than 15 million typed entities and around 10 million relation assertions. Other than knowledge graphs such as DBpedia and YAGO, whose ontologies are comparably simplistic, CaLiGraph also has a rich ontology, comprising more than 200,000 class restrictions. Those two properties -a large A-box and a rich ontology -make it an interesting challenge for benchmarking reasoners. In this paper, we show that a reasoning task which is particularly relevant for CaLiGraph, i.e., the materialization of owl:hasValue constraints into assertions between individuals and between individuals and literals, is insufficiently supported by available reasoning systems. We provide differently sized benchmark subsets of CaLiGraph, which can be used for performance analysis of reasoning systems.

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Section: Given the T-box Above The Derived Axioms According To The De...mentioning

confidence: 99%

The CaLiGraph Ontology as a Challenge for OWL Reasoners

Heist¹,

Paulheim²

2021

Preprint

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“…S 1 An adaptation of the OWL2Bench benchmark [9]. This scenario consists of a university where students register and enroll in a certain program.…”

Section: Streaming Scenariosmentioning

confidence: 99%

“…However, currently, there are no benchmarks or evaluation frameworks available to evaluate and push the research field forward. The RSP community has investigated multiple benchmarks for low expressive streaming tasks [1], while the reasoning community has proposed benchmarks and evaluation frameworks for highly expressive static tasks [9]. Currently, a benchmark or evaluation framework that bridges the two is still missing.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Evaluation Framework for Expressive Stream Reasoning

Bonte

Turck

Ongenae

2021

The Semantic Web: ESWC 2021 Satellite Events

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Stream Reasoning, and more particular RDF Stream Processing (RSP), has focused on processing data streams in a timely manner, while expressive reasoning techniques, such as OWL2 DL, allow to fully model and interpret their domain knowledge. However, expressive reasoning techniques have thus far mostly focused on static data, as it tends to become slow with growing datasets. Expressive Stream Reasoning aims to combine these fields and evaluate expressive reasoning techniques in a timely manner over volatile data streams through various reasoning optimizations. Both expressive reasoning and RSP have benchmarks and frameworks for evaluating and comparing proposed solutions. However, no benchmarks or evaluation frameworks for Expressive Stream Reasoning are currently available.In this paper, we propose OWL2Streams, a resource framework for the evaluation of Expressive Stream Reasoning solutions. We identified challenges and opportunities for optimizations when dealing with expressive reasoning over the combination of streams and static data. OWL2Streams proposes three streaming scenarios, each tackling different challenges.

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“…We are currently implementing this approach and upon its completion, we plan to evaluate its performance on varying size OWL 2 DL ontologies. For this purpose, we would utilize our recently developed benchmark -OWL2Bench (Singh, Bhatia, and Mutharaju 2020).…”

Section: Reinforcement Learning Based Solution For Efficient Tableau ...mentioning

confidence: 99%

Neuro-Symbolic Techniques for Description Logic Reasoning (Student Abstract)

Singh

Mondal²,

Bhatia

et al. 2021

AAAI

Self Cite

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With the goal to find scalable reasoning approaches, neuro-symbolic techniques have gained significant attention. However, the existing approaches do not take into account the inference capabilities of ontology languages that are based on expressive description logic (such as OWL 2). To fill this gap, we propose two approaches: an ontology-based embedding model for theories in EL++ description logic and a reinforcement learning-based solution for efficient tableau-based reasoning on description logic. We describe promising initial results of our efforts towards these directions and lay down the direction for future work.

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OWL2Bench: A Benchmark for OWL 2 Reasoners

Cited by 17 publications

References 14 publications

The CaLiGraph Ontology as a Challenge for OWL Reasoners

The CaLiGraph Ontology as a Challenge for OWL Reasoners

Towards an Evaluation Framework for Expressive Stream Reasoning

Neuro-Symbolic Techniques for Description Logic Reasoning (Student Abstract)

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