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

arXiv: https://arxiv.org/abs/1912.00512 · HTML: https://mlsanity.com/paper/1912.00512/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 19 of 19)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes](https://mlsanity.com/paper/2411.12174/) (2024)
- ["Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision](https://mlsanity.com/paper/2102.01222/) (2021)
- [Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment](https://mlsanity.com/paper/2105.10709/) (2021)
- [A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19](https://mlsanity.com/paper/2411.07163/) (2024)
- [mRNA Design and Optimization with Deep Knowledge-Infused Approach](https://mlsanity.com/paper/2505.23862/) (2025)
- [Towards Explainable Artificial Intelligence in Banking and Financial Services](https://mlsanity.com/paper/2112.08441/) (2021)
- [Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa](https://mlsanity.com/paper/2206.09259/) (2022)
- [Enhancing Cross-Modal Contextual Congruence for Crowdfunding Success using Knowledge-infused Learning](https://mlsanity.com/paper/2402.03607/) (2024)
- [A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2107.10295/) (2021)
- [Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge](https://mlsanity.com/paper/2107.14613/) (2021)
- [PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing](https://mlsanity.com/paper/2308.08371/) (2023)
- [RESTORE: Graph Embedding Assessment Through Reconstruction](https://mlsanity.com/paper/2308.14659/) (2023)
- [Image classification network enhancement methods based on knowledge injection](https://mlsanity.com/paper/2401.04441/) (2024)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)

## Abstract (arXiv)

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. As the incorporation of external knowledge will aid in supervising the learning of features for the model, deep infusion of representational knowledge from knowledge graphs within hidden layers will further enhance the learning process. Although much work remains, we believe that knowledge graphs will play an increasing role in developing hybrid neuro-symbolic intelligent systems (bottom-up deep learning with top-down symbolic computing) as well as in building explainable AI systems for which knowledge graphs will provide scaffolding for punctuating neural computing. In this position paper, we describe our motivation for such a neuro-symbolic approach and framework that combines knowledge graph and neural networks.