Taxonomy is an essential process for gaining, sending, and classifying information, and also creating and using applications in several fields. To minimize humans, work to form the taxonomy learning from scratch and then increase the consistency of the taxonomy, now we suggest an approach to taxonomy learning, called TaxoFinder. TaxoFinder does three stages to construct a taxonomy automatically. Next, it distinguishes notions which are specific to the domain from a corpus of text. Later, it develops a graph describing how these definitions are connected at once depending on their co-occurrences. We will provide a technique for calculating strengths of associative between the concepts as the main method in TaxoFinder, which proves the strength and how tightly they have associated in the graphs, Using their similarities and spatial differences in sentences. Then lastly, have the TaxoFinder which uses a graph-analytical algorithm to trigger a taxonomy. TaxoFinder attempts to construct a taxonomy in such a way that to create a taxonomy, it enhances the associative strengths between the concepts in the graph. We test TaxoFinder on three separate domains using the gold standard evaluation: Mass-meetings emergency response, autism research and disorder domains. We evaluate TaxoFinder as the very effective subsumption method in this development, and it reveals that TaxoFinder was an efficient solution that successfully outperforms the subsumption process.

Knowledge searching, Taxonomy learning, Taxonomy, TaxoFinder, keyword phrases

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