OWL 2: The next step for OWL

Grau, Bernardo Cuenca; Horrocks, Ian; Motik, Boris; Parsia, Bijan; Patel‐Schneider, Peter F.; Sattler, Ulrike

doi:10.1016/j.websem.2008.05.001

Cited by 651 publications

(417 citation statements)

References 11 publications

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“…We note that the generated OWL ontologies comply with OWL2EL [44], an OWL2 profile specifically designed to allow efficient reasoning of large terminologies, which is polynomial in the size of the ontology. While OWL2EL disallows universal quantification on properties, it does allow the inclusion of transitive properties.…”

Section: Methodsmentioning

confidence: 99%

Federated ontology-based queries over cancer data

2012

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BackgroundPersonalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult.ResultsExisting software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included.ConclusionsTo support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures.

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Section: Methodsmentioning

confidence: 99%

Federated ontology-based queries over cancer data

2012

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“…For illustration purposes we only show role subsumption axioms. Consider an axiom of the form R 1 R 2 ⊑ R which is interpreted as: 3 ) which corresponds to the semantics of the fuzzy Datalog rule…”

Section: Mapping Axiomsmentioning

confidence: 99%

“…Ontologies in the Semantic Web are mostly expressed using the OWL (2) Web ontology Language [2,3], which has been standardised by W3C. 3 OWL 2 consists of a family of languages containing namely, OWL 2 DL, OWL 2 QL, OWL 2 EL, and OWL 2 RL.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Extension to the OWL 2 RL Ontology Language

Stoilos

Venetis

Stamou

2015

The Computer Journal

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Fuzzy extensions to Description Logics (DLs) have gained considerable attention the last decade. So far most works on fuzzy DLs have focused on either very expressive languages, like fuzzy OWL and OWL 2, or on highly inexpressive ones, like fuzzy OWL 2 QL and fuzzy OWL 2 EL. To the best of our knowledge a fuzzy extension to the language OWL 2 RL has not been thoroughly studied so far. This language is very relevant since it combines both adequate expressive power as well as efficient reasoning algorithms which can be realised using rule-based (Datalog) technologies. In contrast to previous fuzzy extensions, a fuzzy extension of OWL 2 RL is not a straightforward task for the following reason. The main motivation of OWL 2 RL is that its axioms can be equivalently represented as Datalog rules. Hence, to achieve our goal we need to investigate which OWL 2 RL axioms when interpreted under the fuzzy setting can be transformed to equivalent fuzzy Datalog rules. We show that this is not, in general, possible for all axioms but we show that this "issue" can to a large extent be alleviated. Moreover, we have performed an experimental evaluation with many well-known ontologies which showed that such axioms are not used so often in practice.

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“…For this reason, that language does not include very expressive constructors that are needed to model complex domains. An extensive presentation of the limits of OWL 1, which were partially solved by the definition of its successor OWL 2, can be found in [29]. OWL 2 is an extension of its predecessor with several constructors that emerged to be required for modeling di↵erent domains (e.g., computational biology, social sciences, engineering, etc.).…”

Section: Owl 2 and Its Sublanguagesmentioning

confidence: 99%

“…The OWL 2 specification identifies di↵erent profiles; i.e., subsets of the language to address the requirements of specific domains [29]. OWL 2 DL is the language that is obtained by increasing OWL 1 with the novel operators of the language; hence, it is the most expressive of the OWL 2 family, and obviously the one having highest reasoning complexity.…”

Section: Owl 2 and Its Sublanguagesmentioning

confidence: 99%

OWL 2 modeling and reasoning with complex human activities

Riboni

Bettini

2011

Pervasive and Mobile Computing

136

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In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research e↵orts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of di↵erent intelligent environments.

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OWL 2: The next step for OWL

Cited by 651 publications

References 11 publications

Federated ontology-based queries over cancer data

Federated ontology-based queries over cancer data

A Fuzzy Extension to the OWL 2 RL Ontology Language

OWL 2 modeling and reasoning with complex human activities

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