# Knowledge Graphs and Knowledge Networks: The Story in Brief

arXiv: https://arxiv.org/abs/2003.03623 · HTML: https://mlsanity.com/paper/2003.03623/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation](https://mlsanity.com/paper/2401.11370/) (2024)
- [ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training](https://mlsanity.com/paper/2501.07078/) (2025)
- [Neurosymbolic Value-Inspired AI (Why, What, and How)](https://mlsanity.com/paper/2312.09928/) (2023)
- [SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval](https://mlsanity.com/paper/2412.15443/) (2024)
- [Elastic Weight Consolidation for Knowledge Graph Continual Learning: An Empirical Evaluation](https://mlsanity.com/paper/2512.01890/) (2025)
- [A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2107.10295/) (2021)
- [A Continual Relation Extraction Approach for Knowledge Graph Completeness](https://mlsanity.com/paper/2404.17593/) (2024)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)
- [Post-Training Language Models for Continual Relation Extraction](https://mlsanity.com/paper/2504.05214/) (2025)
- [Comparing Human and Large Language Model Interpretation of Implicit Information](https://mlsanity.com/paper/2604.17085/) (2026)

## Abstract (arXiv)

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.