Open AccessDevelopment of a Machine Learning model for knowledge acquisition, relationship extraction and discovery in Domain Ontology Engineering
Author(s) -
Sivaramakrishnan R Guruvayur*,
R. Suchithra
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6362.098319
Subject(s) - computer science , ontology , knowledge acquisition , knowledge extraction , ontology based data integration , domain (mathematical analysis) , process ontology , domain knowledge , ontology learning , process (computing) , artificial intelligence , upper ontology , information retrieval , suggested upper merged ontology , domain model , ontology engineering , search engine indexing , knowledge representation and reasoning , mathematical analysis , philosophy , mathematics , epistemology , operating system
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