Development of a Machine Learning Model for Knowledge Acquisition, Relationship Extraction and Discovery in Domain Ontology Engineering using Jaccord Relationship Extraction and Neural Network
Sivaramakrishnan R. Guruvayur1, R. Suchithra2

1Sivaramakrishnan R Guruvayur, Department of Computer Science, Jain University, Karnataka, India.
2R. Suchithra, Department of Computer Science, Jain University, Karnataka, India.

Manuscript received on 15 August 2019. | Revised Manuscript received on 21 August 2019. | Manuscript published on 30 September 2019. | PP: 7809-7817 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6362098319/2019©BEIESP | DOI: 10.35940/ijrte.C6362.098319

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Abstract: Creating a fast domain independent ontology through knowledge acquisition is a key problem to be addressed in the domain of knowledge engineering. Updating and validation is impossible without the intervention of domain experts, which is an expensive and tedious process. Thereby, an automatic system to model the ontology has become essential. This manuscript presents a machine learning model based on heterogeneous data from multiple domains including agriculture, health care, food and banking, etc. The proposed model creates a complete domain independent process that helps in populating the ontology automatically by extracting the text from multiple sources by applying natural language processing and various techniques of data extraction. The ontology instances are classified based on the domain. A Jaccord Relationship extraction process and the Neural Network Approval for Automated Theory is used for retrieval of data, automated indexing, mapping and knowledge discovery and rule generation. The results and solutions show the proposed model can automatically and efficiently construct automated Ontology.
Keywords: Automatic Ontology Generation, Jaccord Relationship Extraction, Neural Network, Semantic Web

Scope of the Article: Machine Learning