# A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead

arXiv: https://arxiv.org/abs/2110.08012 · HTML: https://mlsanity.com/paper/2110.08012/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Open-World Knowledge Graph Completion](https://mlsanity.com/paper/1711.03438/) (2017)
- [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://mlsanity.com/paper/2009.11564/) (2020)
- [From Data Fusion to Knowledge Fusion](https://mlsanity.com/paper/1503.00302/) (2015)
- [ICDM 2019 Knowledge Graph Contest: Team UWA](https://mlsanity.com/paper/1909.01807/) (2019)
- [Knowledge Graph Validation](https://mlsanity.com/paper/2005.01389/) (2020)
- [Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph](https://mlsanity.com/paper/2010.08995/) (2020)

## Built on by (most cited first, 4 of 4)

- [Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.12208/) (2025)
- [Spatio-Temporal Relation Learning for Video Anomaly Detection](https://mlsanity.com/paper/2209.13116/) (2022)
- [SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval](https://mlsanity.com/paper/2412.15443/) (2024)
- [The Path to Autonomous Learners](https://mlsanity.com/paper/2211.02403/) (2022)

## Abstract (arXiv)

Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251 . However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation systems, semantic search, etc. The knowledge graph is an emerging technology that allows logical reasoning and uncovers new insights using content along with the context. Thereby, it provides necessary syntax and reasoning semantics that enable machines to solve complex healthcare, security, financial institutions, economics, and business problems. As an outcome, enterprises are putting their effort into constructing and maintaining knowledge graphs to support various downstream applications. Manual approaches are too expensive. Automated schemes can reduce the cost of building knowledge graphs up to 15-250 times. This paper critiques state-of-the-art automated techniques to produce knowledge graphs of near-human quality autonomously. Additionally, it highlights different research issues that need to be addressed to deliver high-quality knowledge graphs