Automated Ontology Construction: Potential for Failure or Success
Despite the dramatic advances in LLM and deep learning, modern AI still faces an ‘epistemological crisis’ of hallucination and explainability. This is because the current approach, which relies solely on statistical patterns (correlations) in data, struggles to handle complex business decisions and clear causal reasoning.
This article proposes ‘Ontology’ and ‘Knowledge Graphs’ as solutions, providing an in-depth analysis of how ontology construction—once a labor-intensive failure case—is now being ‘automated’ through integration with the latest LLM technology and Neuro-Symbolic architecture.
It details the evolution toward ‘System 2 Thinking’ AI—capable of logical verification beyond probabilistic guesswork—and ‘Semantic Integration’ that transcends the physical integration limitations of data lakes. It presents concrete technical solutions and future strategies for those seeking to ensure AI reliability and transparency while building truly data-driven intelligent agents.
