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title: Sheth et al. (2022): Knowledge-Infused Learning
type: reference
tags: :passepartout:architecture:
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:PROPERTIES:
:CREATED:  [2026-05-11 Mon]
:ID:       a56c8e07-9e5b-4070-a0f9-280188ccd6b7
:END:
* Sheth et al. (2022): Knowledge-Infused Learning

Sheth, Gunaratna, Bhatt, and Gaur define Knowledge-infused Learning (KiL) as "combining various types of explicit knowledge with data-driven deep learning techniques." They identify three infusion levels (shallow, semi-deep, deep) and position KiL as "a sweet spot in neuro-symbolic AI."

Passepartout's architecture is a specific implementation of KiL at the deepest infusion level: knowledge is not appended to prompts (shallow) or embedded in fine-tuning (semi-deep). It is a first-class data structure — the symbolic index — that the LLM queries through the archivist and the planner. The knowledge is living: it accumulates, is verified, carries provenance, and evolves through ontology versioning.

Reference: Gaur, M., Gunaratna, K., Bhatt, S., & Sheth, A. (2022). Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI. /IEEE Internet Computing, 26/(4), 5–11.