International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Nov. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 80
Paper Published : 1200
No. of Authors : 3014
  Journal Paper

Paper Title
Measuring The Ontology Level And Class Level Complexity Metrics In The Semantic Web

Abstract
Abstract: Web of data which is increasing daily has to be linked of semantically to reduce the hectic job of the user’s in retrieving the relevant data. Semantic web plays a major role in achieving this with the help of ontology. Ontology describes more about the concepts, axioms, its relationships, properties and its attributes. To retrieve the data the ontology has to extract the information from its deepest roots in its structure. So measuring the ontology is also an important task so that the ontologies can be reused, developed and maintained properly. In this paper, we propose a system to measure the ontology by ontology level metrics and class level metrics function. Both the functions will measure the ontology to the deepest roots efficiently. The measured ontology is also compared with the other ontology structures to prove the efficiency of the measure. Cluster based measure which measures in to the entire length, removes impurity of the tree and navigates in to the edge of the tree. Not only the taxonomic form of the ontology is measured but also the semantic form is also measured to have a better extraction. The method proposed here has a good quality control and it also improves the process of ontology engineering.


Author - Senduru Srinivasulu, P. Sakthivel, Balamurugan. E

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| Published on 2014-05-16
   
   
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