scholarly journals Rule Based Communication Protocol between Social Networks using Semantic Web Rule Language (SWRL)

2016 ◽  
Vol 8 (2) ◽  
pp. 22-29 ◽  
Author(s):  
Enesi F. Aminu ◽  
◽  
Olaide N. Oyelade ◽  
Ibrahim S. Shehu
Keyword(s):  
Social Networks ◽  
Semantic Web ◽  
Communication Protocol ◽  
Rule Based ◽  
Semantic Web Rule Language

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.

Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

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.

Enhancing Social Networks with Agent and Semantic Web Technologies

2012 ◽  
pp. 83-100 ◽  
Author(s):  
Federico Bergenti ◽  
Enrico Franchi ◽  
Agostino Poggi
Keyword(s):  
Social Networks ◽  
Semantic Web ◽  
Collaborative Work ◽  
Multi Agent Systems ◽  
Semantic Web Technologies ◽  
Agent Based ◽  
Web Technologies ◽  
Agent Systems ◽  
Multi Agent ◽  
Relevant Work

In this chapter, the authors describe the relationships between multi-agent systems, social networks, and the Semantic Web within collaborative work; they also review how the integration of multi-agent systems and Semantic Web technologies and techniques can be used to enhance social networks at all scales. The chapter first provides a review of relevant work on the application of agent-based models and abstractions to the key ingredients of our work: collaborative systems, the Semantic Web, and social networks. Then, the chapter discusses the reasons current multi-agent systems and their foreseen evolution might be a fundamental means for the realization of the future Semantic Social Networks. Finally, some conclusions are drawn.

Sign in / Sign up

Export Citation Format

Share Document