# Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing

arXiv: https://arxiv.org/abs/1802.05930 · HTML: https://mlsanity.com/paper/1802.05930/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Relational Memory Augmented Language Models](https://mlsanity.com/paper/2201.09680/) (2022)
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- [Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding](https://mlsanity.com/paper/2106.07250/) (2021)
- [Preference Learning for AI Alignment: a Causal Perspective](https://mlsanity.com/paper/2506.05967/) (2025)
- [GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction](https://mlsanity.com/paper/2210.01301/) (2022)
- [UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction](https://mlsanity.com/paper/2411.07019/) (2024)
- [Does Commonsense help in detecting Sarcasm?](https://mlsanity.com/paper/2109.08588/) (2021)
- [CKG: Dynamic Representation Based on Context and Knowledge Graph](https://mlsanity.com/paper/2212.04909/) (2022)
- [Predictive AI with External Knowledge Infusion: Datasets and Benchmarks for Stock Markets](https://mlsanity.com/paper/2504.20058/) (2025)
- [Knowledge-Aware Neural Networks for Medical Forum Question Classification](https://mlsanity.com/paper/2109.13141/) (2021)
- [ActKnow: Active External Knowledge Infusion Learning for Question Answering in Low Data Regime](https://mlsanity.com/paper/2112.09423/) (2021)
- [Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus](https://mlsanity.com/paper/2206.07472/) (2022)
- [Development of a Knowledge Graph Embeddings Model for Pain](https://mlsanity.com/paper/2308.08904/) (2023)
- [How to Understand Named Entities: Using Common Sense for News Captioning](https://mlsanity.com/paper/2403.06520/) (2024)

## Abstract (arXiv)

Machine Learning has been the quintessential solution for many AI problems, but learning is still heavily dependent on the specific training data. Some learning models can be incorporated with a prior knowledge in the Bayesian set up, but these learning models do not have the ability to access any organised world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with News20, DBPedia datasets and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained well with substantially less amount of labeled training data, when it has access to organised world knowledge in the form of knowledge graph.