Ontology Learning Based on Word Embeddings for Text Big Data Extraction

摘要: Big Data term describes data that exists everywhere in humongous volumes, raw forms, and heterogenous types. Unstructured uncategorized forms 95% of big data. Text lacks to efficiently extract domain-relevant a suitable time. Thus, text stills barrier for integration subsequently analytics. Because can’t consider its process preparing On the other side, ontology represents information knowledge graph schema provides shareable, reusing domain-specific fits needs extracting domain relevant So, this paper proposes an learning (OL) methodology extraction. OL aims algorithms, techniques, tools automatic construction from text. The proposed method exploits deep approach i.e., word embeddings, advanced hierarchical clustering BIRCH. utilization embeddings improve quality extraction reduce processing Also, unsupervisory learns massive amount unlabeled This great benefit solves analytical challenge In evaluation, precision, recall, f – value work running time performance are measured. is evaluated by comparing results with gold standard datasets results. Experimental evaluation demonstrate