Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation
This work tackles scalability issues in semantic Web applications, but it appears incremental as it builds on existing segmentation approaches without specifying novel breakthroughs.
The paper addresses the challenge of managing large Web ontologies by proposing a segmentation method that extracts horizontal layers or generations from existing ontologies, aiming to reduce complexity and improve reasoning efficiency.
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 ontologies as well as its usefulness. The segmentation method extracts from an existing ontology a segment that represents a layer or a generation in the existing ontology; i.e. a horizontally extraction. The extracted segment should be itself an ontology.