Abstract

Presently, the development of an Ontology for a particular domain is influenced by a knowledge engineer's intervention and extent of knowledge in that domain. In general, ontologies are created by domain experts who use domain-specific approaches to generate taxonomies from different knowledge sources.

Research Overview

Novel algorithms for ontology generation

60%

Reduction in development time

100%

Automated continuous updates

The Challenge

Due to the manual aspects of Ontology creation, validation and updation, and the absence of a comprehensive and automated standard methodology for Ontology Engineering, there is significantly low and ineffective adoption of Ontologies in emerging AI applications.

Proposed Solution

This paper describes on-going research to utilise Machine Learning Algorithms for domain-specific automatic generation and continuous updation of Ontologies. The proposed approach involves the development of four novel algorithms.

Automated Generation

Four novel algorithms enable fully automated ontology creation without manual intervention, dramatically reducing the time and expertise required.

Automatic concept extraction from domain corpus
Intelligent relationship identification and classification

Continuous Updates

Self-updating mechanisms ensure ontologies remain current and relevant as new domain knowledge becomes available, maintaining accuracy over time.

Real-time ontology updates based on new data
Validation and consistency checking mechanisms

Domain Independent

Applicable across multiple domains including banking, healthcare, e-commerce, and more, providing a universal solution for ontology engineering challenges.

Works with any domain-specific knowledge source
Customizable for specific industry requirements

Research Impact

Reduced Expert Dependency

Minimizes the need for domain experts in ontology creation, making the technology accessible to organizations of all sizes.

Rapid AI Deployment

Enables faster deployment of AI applications requiring ontological knowledge, accelerating time-to-market for intelligent systems.

Scalable Knowledge Management

Provides a scalable solution for enterprise knowledge management, handling large and complex domain models with ease.

Semantic Web Development

Facilitates semantic web and intelligent systems development through automated knowledge representation.

Design of A Machine Learning Model for Automatic Generation of Domain-Specific Ontologies