N-Dimensional Matrix-Based Ontology

This article describes how the assessment of semantic similarities between word pairs is an important component of understanding text which enables processing, classifying and structuring of textual resources. For this purpose, an ontology is a powerful technique when applied to compute similarity. In this article, the authors propose a novel model to represent an ontology in which an N-dimensional matrix is applied, called an N-dimensional matrix-based ontology. This matrix-based ontology attempts to decrease the time complexity of computation. Second, a new semantic similarity measure is introduced and is performed on the N-dimensional matrix-based ontology. Third, the validation of the result of the N-dimensional matrix-based ontology is compared with related studies comparing two well-known benchmarks. The results reveal that in an N-dimensional matrix-based ontology with increasing N, the accuracy of the proposed semantic similarity measure is increased. Moreover, a matrix-based ontology decreases the time complexity when compared to a graph-based ontology.

Named Data Networking

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

PROSE

The study of paraconsistent reasoning with ontologies is especially important for the Semantic Web since knowledge is not always perfect within it. However, classical OWL reasoners cannot support reasoning with inconsistent ontologies. In this article, the authors present a plugin-based framework called prose to provide rich paraconsistent reasoning services for OWL ontologies, whose architecture contains the three following parts: a classical OWL reasoner, a multi-valued transformer, and an OWL API connecting with them. Within the proposed framework prose, they implement different multi-valued paraconsistent reasoning in the OWL. Moreover, they select three popular classical OWL reasoners and two typical kinds of reasoning services for users. As the authors excepted, prose does exactly enable current classical OWL reasoners to tolerate inconsistency in a simple and convenient way. Finally, they evaluate the three reasoners in a united framework (prose) and, as a result, those results can amend the analysis of the three reasoners on inconsistent ontologies.