Rule-Based OWL Ontology Reasoning Systems

2010 ◽  
pp. 124-148 ◽  
Author(s):  
Georgios Meditskos ◽  
Nick Bassiliades
Keyword(s):  
Semantic Web ◽  
Description Logic ◽  
Formal Semantics ◽  
Web Ontology Language ◽  
Rule Engine ◽  
Basic Principles ◽  
Rule Based ◽  
Ontology Language ◽  
Open Nature ◽  
The Web

This chapter is focused on the basic principles behind the utilization of rules in order to perform reasoning about the Web Ontology Language (OWL), a Description Logic-based language that is the W3C recommendation for creating and sharing ontologies in the Semantic Web. More precisely, we elaborate on the entailment-based OWL reasoning (EBOR) paradigm, which is based on the utilization of RDF/ RDFS and OWL entailment rules that run on a rule engine, applying the formal semantics of the ontology language. To this end, seven EBOR systems are described and compared, analyzing the different approaches. Despite the closed rule environment, which comes in contrast with the open nature of the Semantic Web, and the fact that OWL semantics are partially mapped into rules, the rule-based OWL reasoning paradigm can give great potentials in the Semantic Web, enabling the utilization of rule engines on top of ontology information.

SWRLx

2018 ◽  
pp. 348-367
Author(s):  
Souad Bouaicha ◽  
Zizette Boufaida
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Object Oriented ◽  
Web Ontology Language ◽  
Rule Engine ◽  
Ontology Language ◽  
New Knowledge ◽  
Semantic Web Rule Language

Although OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) add considerable expressiveness to the Semantic Web, they do have expressive limitations. For some reasoning problems, it is necessary to modify existing knowledge in an ontology. This kind of problem cannot be fully resolved by OWL and SWRL, as they only support monotonic inference. In this paper, the authors propose SWRLx (Extended Semantic Web Rule Language) as an extension to the SWRL rules. The set of rules obtained with SWRLx are posted to the Jess engine using rewrite meta-rules. The reason for this combination is that it allows the inference of new knowledge and storing it in the knowledge base. The authors propose a formalism for SWRLx along with its implementation through an adaptation of different object-oriented techniques. The Jess rule engine is used to transform these techniques to the Jess model. The authors include a demonstration that demonstrates the importance of this kind of reasoning. In order to verify their proposal, they use a case study inherent to interpretation of a preventive medical check-up.

scholarly journals Overview and Utilization of the NCI Thesaurus

2004 ◽  
Vol 5 (8) ◽  
pp. 648-654 ◽  
Author(s):  
Gilberto Fragoso ◽  
Sherri de Coronado ◽  
Margaret Haber ◽  
Frank Hartel ◽  
Larry Wright
Keyword(s):  
National Cancer Institute ◽  
Description Logic ◽  
Biological Processes ◽  
Gene Products ◽  
Collaborative Effort ◽  
Web Ontology Language ◽  
Ontology Language ◽  
File Formats ◽  
History Of ◽  
The Web

The NCI Thesaurus is a reference terminology covering areas of basic and clinical science, built with the goal of facilitating translational research in cancer. It contains nearly 110 000 terms in approximately 36000 concepts, partitioned in 20 subdomains, which include diseases, drugs, anatomy, genes, gene products, techniques, and biological processes, among others, all with a cancer-centric focus in content, and originally designed to support coding activities across the National Cancer Institute. Each concept represents a unit of meaning and contains a number of annotations, such as synonyms and preferred name, as well as annotations such as textual definitions and optional references to external authorities. In addition, concepts are modelled with description logic (DL) and defined by their relationships to other concepts; there are currently approximately 90 types of named relations declared in the terminology. The NCI Thesaurus is produced by the Enterprise Vocabulary Services project, a collaborative effort between the NCI Center for Bioinformatics and the NCI Office of Communications, and is part of the caCORE infrastructure stack (http://ncicb.nci.nih.gov/NCICB/core). It can be accessed programmatically through the open caBIO API and browsed via the web (http://nciterms.nci.nih.gov). A history of editing changes is also accessible through the API. In addition, the Thesaurus is available for download in various file formats, including OWL, the web ontology language, to facilitate its utilization by others.

OWL-Based Description for Agent

2011 ◽  
Vol 217-218 ◽  
pp. 1218-1223
Author(s):  
Gang Wang ◽  
Jie Lin ◽  
Qing Qi Long ◽  
Zhi Juan Hu
Keyword(s):  
Semantic Web ◽  
Early Development ◽  
Formal Specification ◽  
Ontology Development ◽  
Web Ontology Language ◽  
Development Stages ◽  
Ontology Language ◽  
Development Tools ◽  
Web Community ◽  
The Web

This paper presents a detailed formal specification of agents and their properties and abilities,based on the Web Ontology Language (OWL). It allows an agent to be specified entirely using standard mark-up languages from the Semantic Web community, namely RDF, RDF Schemaand OWL. The basic agent components are identified and their implementation using ontology development tools is described.The description improves consistency, interoperability and maintainability of agent program. Therefore,the design errors in the early development stages could be efficiently detected and avoided.

ENHANCING OWL ONTOLOGIES WITH RELATION SEMANTICS

2008 ◽  
Vol 18 (03) ◽  
pp. 327-356 ◽  
Author(s):  
CARTIK R. KOTHARI ◽  
DAVID J. RUSSOMANNO
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Web Ontology Language ◽  
Intrinsic Nature ◽  
Domain Specific ◽  
Ontology Language ◽  
High Level ◽  
Explicit Relation ◽  
Potential Use ◽  
The Web

The OWL Enhance prototype has been developed to augment ontologies implemented using the Web Ontology Language (OWL) with richer relation semantics. This prototype interactively elicits knowledge from providers to describe the intrinsic nature of relations and appends these elicited semantics to definitions of relations in OWL ontologies. Benefits from the explicit specification of the intrinsic nature of relations in ontologies include the development of quantitative techniques for the estimation of similarities among relations and attribute exploration techniques to create relation taxonomies. Examples of these techniques have been implemented in modules of the OWL Enhance prototype to demonstrate the utility of explicit relation semantics. Results from testing these modules on high-level and domain-specific ontologies are presented and analyzed with respect to the potential use of relation semantics to increase the fidelity of knowledge representation, as well as the potential for reuse and interoperability of knowledge on the Semantic Web.

scholarly journals Pay-As-You-Go Description Logic Reasoning by Coupling Tableau and Saturation Procedures

2015 ◽  
Vol 54 ◽  
pp. 535-592 ◽  
Author(s):  
Andreas Steigmiller ◽  
Birte Glimm
Keyword(s):  
Description Logic ◽  
Description Logics ◽  
The Other ◽  
Web Ontology Language ◽  
Ontology Language ◽  
Specific Description ◽  
Wide Range ◽  
Detailed Evaluation ◽  
Logic Reasoning ◽  
The Web

Nowadays, saturation-based reasoners for the OWL EL profile of the Web Ontology Language are able to handle large ontologies such as SNOMED very efficiently. However, it is currently unclear how saturation-based reasoning procedures can be extended to very expressive Description Logics such as SROIQ--the logical underpinning of the current and second iteration of the Web Ontology Language. Tableau-based procedures, on the other hand, are not limited to specific Description Logic languages or OWL profiles, but even highly optimised tableau-based reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau- and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance for a wide range of ontologies.

An Ontology Based Representation of Software Design Patterns

2011 ◽  
pp. 258-279 ◽  
Author(s):  
Jens Dietrich ◽  
Chris Elgar
Keyword(s):  
Software Engineering ◽  
Semantic Web ◽  
Design Patterns ◽  
Web Ontology Language ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Ontology Language ◽  
Software Design Patterns ◽  
Formal Definitions ◽  
The Web

This chapter introduces an approach to define Design patterns using semantic Web technologies. For this purpose, a vocabulary based on the Web ontology language OWL is developed. Design patterns can be defined as RDF documents instantiating this vocabulary, and can be published as resources on standard Web servers. This facilitates the use of patterns as knowledge artefacts shared by the software engineering community. The instantiation of patterns in programs is discussed, and the design of a tool is presented that can x-ray programs for pattern instances based on their formal definitions.

Dynamic, Customized Workflow Using BDI Agent and Semantic Web Service

2011 ◽  
Vol 135-136 ◽  
pp. 477-483
Author(s):  
Chih Hao Liu ◽  
Jason Jen Yen Chen
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Semantic Information ◽  
Semantic Web Services ◽  
Web Ontology Language ◽  
Semantic Web Service ◽  
Ontology Language ◽  
Bdi Agent ◽  
The World ◽  
The Web

As the Web gradually evolves into the semantic web, the World Wide Web consortium (W3C) recommends that web ontology language (OWL) be used to encode semantic information content over the Web. Semantic web is an essential infrastructure to enhance Web to obtain better integration of information and intelligent use of web resources. Moreover, a web service is annotated by web ontology language for service (OWL-S) to form a semantic web service that, however, is a static description. The OWL-S based semantic web services thus are reactively invoked by users. How to dynamically coordinate, composite, or discover the services is an important issue.

scholarly journals A Rule-Based Reasoner for Underwater Robots Using OWL and SWRL

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3481 ◽  
Author(s):  
Zhaoyu Zhai ◽  
José-Fernán Martínez Ortega ◽  
Néstor Lucas Martínez ◽  
Pedro Castillejo
Keyword(s):  
Semantic Web ◽  
Model Transformations ◽  
Web Ontology Language ◽  
Underwater Robots ◽  
Rule Based ◽  
Ontology Language ◽  
General Rules ◽  
Complex Models ◽  
Semantic Web Rule Language ◽  
Information Heterogeneity

Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement “If … Then … Else …”. Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.

A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation

2020 ◽  
Vol 16 (1) ◽  
pp. 87-115
Author(s):  
Nick Bassiliades
Keyword(s):  
Semantic Web ◽  
Query Language ◽  
Markup Language ◽  
Web Ontology Language ◽  
Rule Based ◽  
Horn Logic ◽  
Industry Standard ◽  
Ontology Language ◽  
Widespread Acceptance ◽  
Semantic Web Rule Language

Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not going to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPARQL Inferencing Notation (SPIN) has become a de-facto industry standard to represent SPARQL rules and constraints on semantic web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this article, we argue that the life of existing SWRL rule-based ontology applications can be prolonged by converting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.

SWRLx

2016 ◽  
Vol 12 (2) ◽  
pp. 53-69 ◽  
Author(s):  
Souad Bouaicha ◽  
Zizette Boufaida
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Object Oriented ◽  
Web Ontology Language ◽  
Rule Engine ◽  
Ontology Language ◽  
New Knowledge ◽  
Semantic Web Rule Language

Although OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) add considerable expressiveness to the Semantic Web, they do have expressive limitations. For some reasoning problems, it is necessary to modify existing knowledge in an ontology. This kind of problem cannot be fully resolved by OWL and SWRL, as they only support monotonic inference. In this paper, the authors propose SWRLx (Extended Semantic Web Rule Language) as an extension to the SWRL rules. The set of rules obtained with SWRLx are posted to the Jess engine using rewrite meta-rules. The reason for this combination is that it allows the inference of new knowledge and storing it in the knowledge base. The authors propose a formalism for SWRLx along with its implementation through an adaptation of different object-oriented techniques. The Jess rule engine is used to transform these techniques to the Jess model. The authors include a demonstration that demonstrates the importance of this kind of reasoning. In order to verify their proposal, they use a case study inherent to interpretation of a preventive medical check-up.

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