# Healthcare Knowledge Graph Construction: State-of-the-art, open issues, and opportunities

arXiv: https://arxiv.org/abs/2207.03771 · HTML: https://mlsanity.com/paper/2207.03771/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 157 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 17 of 17)

- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation](https://mlsanity.com/paper/2007.00576/) (2020)
- [Embedding Models for Episodic Knowledge Graphs](https://mlsanity.com/paper/1807.00228/) (2018)
- [Deep learning for drug repurposing: methods, databases, and applications](https://mlsanity.com/paper/2202.05145/) (2022)
- [Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph](https://mlsanity.com/paper/1910.01116/) (2019)
- [Relational Learning Analysis of Social Politics using Knowledge Graph Embedding](https://mlsanity.com/paper/2006.01626/) (2020)
- [Applications of knowledge graphs for food science and industry](https://mlsanity.com/paper/2107.05869/) (2021)
- [Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach](https://mlsanity.com/paper/2003.10715/) (2020)
- [Personal Health Knowledge Graphs for Patients](https://mlsanity.com/paper/2004.00071/) (2020)
- [Coronavirus Knowledge Graph: A Case Study](https://mlsanity.com/paper/2007.10287/) (2020)
- [Ontology-based Approach for Identifying the Credibility Domain in Social Big Data](https://mlsanity.com/paper/1801.01624/) (2018)
- [Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter](https://mlsanity.com/paper/1908.02571/) (2019)
- [Leveraging Big Data Analytics in Healthcare Enhancement: Trends, Challenges and Opportunities](https://mlsanity.com/paper/2004.09010/) (2020)
- [Applying Vector Space Model (VSM) Techniques in Information Retrieval for Arabic Language](https://mlsanity.com/paper/1801.03627/) (2018)

## Built on by (most cited first, 2 of 2)

- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Transforming Expert Knowledge into Scalable Ontology via Large Language Models](https://mlsanity.com/paper/2506.08422/) (2025)

## Abstract (arXiv)

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.