Development of a Machine Learning Model for Knowledge Acquisition, Relationship Extraction and Discovery in Domain Ontology Engineering

Research focus

The research presents a machine-learning model for automated knowledge acquisition across multiple domains. It uses natural language processing and data extraction to populate ontologies, classify instances and support relationship extraction, indexing, mapping, knowledge discovery and rule generation.

Agentic AI as a knowledge discovery orchestrator

Agentic systems can coordinate ingestion, extraction, validation and reasoning across heterogeneous sources. A knowledge agent can identify missing relationships, ask for clarification, compare evidence, update approved knowledge and make that context available to customer-service, onboarding and recommendation agents.

Agentic AI perspective

Modern insurance intelligence needs more than isolated models. Agentic AI connects domain knowledge, reasoning, tools, data and human approvals so that AI can move from prediction to governed execution.

UnderstandGround decisions in domain ontology, customer context and trusted enterprise data.

ReasonCombine models, rules and knowledge graphs to evaluate evidence and intent.

ActCoordinate next-best actions, updates and handoffs with traceability and human control.

What this means for insurers

Build reusable domain intelligence instead of isolated AI use cases.
Connect structured and unstructured data to contextual decisioning.
Continuously improve knowledge and recommendations with governed feedback loops.
Keep humans in the loop for high-impact decisions and exceptions.

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