Using Vampire to Reason with OWL

Tsarkov, Dmitry; Riazanov, Alexandre; Bechhofer, Sean; Horrocks, Ian

doi:10.1007/978-3-540-30475-3_33

Cited by 49 publications

(40 citation statements)

References 15 publications

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“…Let KB be an owl knowledge base, defined in the usual way, possibly containing swrl rules, and T a function that translates such knowledge bases to their first-order logic equivalent [2].…”

Section: Semantics Of Tasksmentioning

confidence: 99%

Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL

Elenius

Ford

Denker

2009

Lecture Notes in Computer Science

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Abstract. Military training and testing events are highly complex affairs, potentially involving dozens of legacy systems that need to interoperate in a meaningful way. There are superficial interoperability concerns (such as two systems not sharing the same messaging formats), but also substantive problems such as different systems not sharing the same understanding of the terrain, positions of entities, and so forth. We describe our approach to facilitating such events: describe the systems and requirements in great detail using ontologies, and use automated reasoning to automatically find and help resolve problems. The complexity of our problem took us to the limits of what one can do with owl, and we needed to introduce some innovative techniques of using and extending it. We describe our novel ways of using swrl and discuss its limitations as well as extensions to it that we found necessary or desirable. Another innovation is our representation of hierarchical tasks in owl, and an engine that reasons about them. Our task ontology has proved to be a very flexible and expressive framework to describe requirements on resources and their capabilities in order to achieve some purpose.

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“…Let KB be an owl knowledge base, defined in the usual way, possibly containing swrl rules, and T a function that translates such knowledge bases to their first-order logic equivalent [2].…”

Section: Semantics Of Tasksmentioning

confidence: 99%

Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL

Elenius

Ford

Denker

2009

Lecture Notes in Computer Science

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“…However, while it is useful to have a prototype that can be used for illustrative and test purposes, the effectiveness of such a naive approach must be open to question with larger SWRL ontologies. In [55] it was shown that, when using the same translation approach to reason with OWL DL ontologies, performance could be greatly improved by using a so-called "relevant only" translation. The key idea is that when ontologies are translated, Vampire receives all of the axioms that occur in the ontology, whereas usually only a small fraction of them are actually relevant to a given subsumption or inconsistency problem.…”

Section: Performance and Optimisationmentioning

confidence: 99%

“…The definition of relevance given in [55] can be extended to SWRL by treating rules in the same way as general concept inclusion axioms (GCIs). A concept or role expression depends on every concept or role that occurs in it, and a concept or role C depends on a concept or role D if D occurs in the definition of C. In addition, a concept C depends on every GCI and rule in the ontology.…”

Section: Performance and Optimisationmentioning

confidence: 99%

OWL rules: A proposal and prototype implementation

Horrocks

Patel‐Schneider

Bechhofer

et al. 2005

Journal of Web Semantics

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254

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Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present SWRL (the Semantic Web Rules Language), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.

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“…Experiments into their use for classifying DL-based ontologies, using a naive translation of DL formulas into first-order formulas, have also shown encouraging results [45].…”

Section: Introductionmentioning

confidence: 98%

“…It is claimed in [45] that first-order theorem provers are not efficient for reasoning with ontologies based on description logics compared to specialised description logic reasoners. However, the development of more expressive ontology languages requires the use of theorem provers able to reason with full first-order logic and even its extensions.…”

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confidence: 99%

Reasoning Support for Expressive Ontology Languages Using a Theorem Prover

Horrocks

Воронков

2006

Lecture Notes in Computer Science

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Abstract. It is claimed in [45] that first-order theorem provers are not efficient for reasoning with ontologies based on description logics compared to specialised description logic reasoners. However, the development of more expressive ontology languages requires the use of theorem provers able to reason with full first-order logic and even its extensions. So far, theorem provers have extensively been used for running experiments over TPTP containing mainly problems with relatively small axiomatisations. A question arises whether such theorem provers can be used to reason in real time with large axiomatisations used in expressive ontologies such as SUMO. In this paper we answer this question affirmatively by showing that a carefully engineered theorem prover can answer queries to ontologies having over 15,000 first-order axioms with equality. Ontologies used in our experiments are based on the language KIF, whose expressive power goes far beyond the description logic based languages currently used in the Semantic Web.State-of-the-art theorem provers for first-order logic (FOL) are highly sophisticated and efficient programs. Moreover, they are very flexible tools and can be tuned to a number of applications. For example, Vampire [35] provides a large collection of parameters that can be used to give better performance for various classes of applications. In addition, Vampire implements a number of literal selection functions and internally contains a library for defining such functions in a simple way; this makes it possible to simulate various proof-search algorithms and even provide decision procedures for decidable classes of first-order logic (see, e.g., [23]).However, there was a common belief that provers like Vampire cannot directly be used for efficient reasoning with very large ontologies using expressive languages such as KIF [14] for two reasons. Firstly, these provers are optimised for reasoning with relatively small axiomatisations. Secondly, they do not support some extensions of FOL required in KIF.In this paper we describe an adaptation of Vampire to support reasoning for expressive ontology languages and present experimental results which show that it can be used for efficient reasoning with large ontologies using extensions of the first-order language.The authors are partially supported by grants from EPSRC. This paper is structured as follows. In Section 1 we briefly overview expressive languages for ontologies, including the language KIF, and FO provers. In Section 2 we describe the adaptation of Vampire for reasoning with large ontologies. For our experiments we selected ontologies implemented in KIF since they offer a high degree of sophistication compared to ontologies using Description Logic (DL) based languages, and there are publicly available KIF-based ontologies containing thousands of FO formulas with equality. However, our adaptation is quite general, and could be used for other expressive ontology languages.Note that there is no way to compare Vampire with description logic prove...

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Using Vampire to Reason with OWL

Cited by 49 publications

References 15 publications

Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL

Reasoning about Resources and Hierarchical Tasks Using OWL and SWRL

OWL rules: A proposal and prototype implementation

Reasoning Support for Expressive Ontology Languages Using a Theorem Prover

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