Summary of Accelerating Medical Knowledge Discovery Through Automated Knowledge Graph Generation and Enrichment, by Mutahira Khalid et al.
Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment
by Mutahira Khalid, Raihana Rahman, Asim Abbas, Sushama Kumari, Iram Wajahat, Syed Ahmad Chan Bukhari
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative approach called Medical Knowledge Graph Automation (M-KGA) to overcome challenges in automating and completing knowledge graphs. Despite efforts to automate KGs using expert-created ontologies, gaps in connectivity remain prevalent. M-KGA leverages user-provided medical concepts and enriches them semantically using BioPortal ontologies, enhancing the completeness of knowledge graphs through pre-trained embeddings. The approach introduces two methodologies: a cluster-based approach and a node-based approach. Testing on 100 frequently occurring medical concepts in Electronic Health Records (EHRs) demonstrates promising results, indicating M-KGA’s potential to address limitations of existing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super powerful tool that helps organize and understand lots of medical information. This paper is about creating a new way to make this tool better by using special concepts and connecting them in a smart way. Even though people have been trying to make this tool easier to use, there are still some problems that need solving. The new approach is called Medical Knowledge Graph Automation (M-KGA) and it’s designed to help fix these issues. M-KGA uses special medical ideas and makes connections between them using very smart computer techniques. In tests with lots of medical data, this new way showed great promise in making the tool more useful. |
Keywords
» Artificial intelligence » Knowledge graph
