2017
DOI: 10.1007/978-3-319-64719-7_7
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Towards Classification of Web Ontologies Using the Horizontal and Vertical Segmentation

Abstract: Abstract. The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big files in memory, the time it needs to download such files and especially the time it needs to make reasoning on them. We discuss in this paper approaches for segmenting such big Web onto… Show more

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Cited by 8 publications
(3 citation statements)
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“…The extracted ontology will consist only of the required knowledge related to the study domain, and will be smaller than the original ontology. For example, Nejjahi et al 94 presented a new ontology segmentation technique based on ontology classes and properties to reduce the size of an ontology model, allowing easier download for users. In addition, Mohsen et al 95 proposed an algorithm that applied power sets to compress the ontology, and the results showed that when redundant concepts were eliminated, the performance of an ontology was slightly affected.…”
Section: Discussionmentioning
confidence: 99%
“…The extracted ontology will consist only of the required knowledge related to the study domain, and will be smaller than the original ontology. For example, Nejjahi et al 94 presented a new ontology segmentation technique based on ontology classes and properties to reduce the size of an ontology model, allowing easier download for users. In addition, Mohsen et al 95 proposed an algorithm that applied power sets to compress the ontology, and the results showed that when redundant concepts were eliminated, the performance of an ontology was slightly affected.…”
Section: Discussionmentioning
confidence: 99%
“…After preparing the dataset, we import it into Weka software, which contains tools for data preparation, classification [16], clustering, association rule exploration, visualization [17] and Similarity [18]. We used the six most commonly used classifiers to classify binary datasets (SVM, KNN, ANN, Logistic Regression, Naïve Bayes, Decision Tree).…”
Section: Machine Learning Algorithmsmentioning
confidence: 99%
“…Such kind of ontologies include the Gene Ontology (GO) [5], an ontology of the biological area. In [6], the authors used this classification to reduce the size of ontologies by extracting only segments that correspond to a criterion.…”
Section: Types Of Ontologiesmentioning
confidence: 99%