Summary of Deep Outdated Fact Detection in Knowledge Graphs, by Huiling Tu et al.
Deep Outdated Fact Detection in Knowledge Graphs
by Huiling Tu, Shuo Yu, Vidya Saikrishna, Feng Xia, Karin Verspoor
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Digital Libraries (cs.DL); Machine Learning (cs.LG)
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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Medium Difficulty Summary In this paper, researchers introduce DEAN, a deep learning-based framework designed to detect outdated facts in knowledge graphs (KGs). The issue of outdated facts is a significant challenge for KGs as real-world information evolves. Existing solutions rely on manual recognition, which can be time-consuming and inaccurate. DEAN captures implicit structural information among facts by modeling entities and relations comprehensively. It employs a contrastive approach using a pre-defined Relations-to-Nodes (R2N) graph weighted by the number of entities to uncover latent out-of-date information. Experimental results show that DEAN outperforms state-of-the-art baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure knowledge graphs are accurate and up-to-date. Right now, people have to manually check facts in these graphs, which can be very time-consuming. The researchers developed a new way to find outdated facts using artificial intelligence. They called it DEAN, short for Deep outdatEd fAct detectioN. DEAN looks at how different pieces of information are connected and uses this information to find old or wrong facts. This helps make sure the knowledge graph is reliable. |
Keywords
* Artificial intelligence * Deep learning * Knowledge graph
