Summary of Cleangraph: Human-in-the-loop Knowledge Graph Refinement and Completion, by Tyler Bikaun et al.
CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion
by Tyler Bikaun, Michael Stewart, Wei Liu
First submitted to arxiv on: 7 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces CleanGraph, a web-based tool for refining and completing knowledge graphs. The accuracy of these graphs is crucial for applications like question-answering systems and information retrieval. However, ensuring the quality of automatically extracted triples from large or low-quality datasets can be challenging. CleanGraph allows users to perform CRUD operations, apply models as plugins, and enhance the integrity and reliability of their graph data. The tool enables users to create, read, update, and delete graph elements while utilizing machine learning-based models for refinement and completion tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CleanGraph is a special tool that helps make sure knowledge graphs are correct. Knowledge graphs are like super-long lists of facts that computers use to answer questions. But sometimes these lists can get messy or include fake information. CleanGraph lets users fix mistakes, add new facts, and make sure the list is accurate. This is important because it makes it easier for computers to give good answers to questions. |
Keywords
* Artificial intelligence * Machine learning * Question answering
