A Practical Framework for the Semantic Web Ontology Learning

Abstract

The formal ontologies that organize underlying data are extensively utilized by the Semantic Web to achieve complete and portable machine comprehension. Because of this, the Semantic Web's success is heavily dependent on the spread of ontologies, which calls for quick and simple ontology architecture and the avoidance of a knowledge accumulation bottleneck. The ontology engineer's ability to build ontologies is significantly aided by ontology learning. The goal of the ontology learning approach is to enable a collaborative, semi-automatic ontology engineering process. We suggest here involves several complimentary fields that draw on various kinds of unorganized, semi-structured, and completely structured data. By importing, extracting, pruning, refining, and evaluating ontologies, our ontology learning system provides the ontology architect with a wide range of composed instruments for cosmology demonstration. Notwithstanding the general structure and engineering, we show in this paper a few illustrative methods in the metaphysics learning cycle that we have carried out in our philosophy learning climate, Text-To-Onto, for example, cosmology gaining from free text, from word references, or heritage ontologies, and we allude to others that should be utilized related with these to complete the full architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents.