Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning

Understand

Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.

• Deep learning that primarily constitutes neural computing stream in AI has shown significant advances in probabilistically learning latent patterns using a multi-layered network of computational nodes (i.e., neurons/hidden units).
• Structured knowledge that underlies symbolic computing approaches and often supports reasoning, has also seen significant growth in recent years, in the form of broad-based (e.g., DBPedia, Yago) and domain, industry or application specific knowledge graphs.
• A common substrate with careful integration of the two will raise opportunities to develop neuro-symbolic learning approaches for AI, where conceptual and probabilistic representations are combined.