Summary of Decrl: a Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach, by Qian Chen and Ling Chen
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
by Qian Chen, Ling Chen
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. Existing approaches fail to capture high-order correlations in TKGs, but our proposed Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning approach (DECRL) addresses this limitation. DECRL features a deep evolutionary clustering module that captures the evolution of high-order correlations among entities and ensures precise alignment across timestamps through a cluster-aware unsupervised alignment mechanism. Additionally, an implicit correlation encoder is introduced to capture latent correlations between clusters under a global graph’s guidance. Our approach achieves state-of-the-art performances on seven real-world datasets, outperforming the best baseline by 9.53%, 12.98%, 10.42%, and 14.68% in MRR, Hits@1, Hits@3, and Hits@10, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how things change over time. It’s called Temporal Knowledge Graph representation learning. Right now, there are some problems with the way we do this. We can’t capture all the connections between different things that happen at different times. To fix this, the authors created a new approach called DECRL (Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning). It has three main parts: one that helps us understand how things change over time, another that makes sure our answers match up correctly across different times, and a third that shows us hidden connections between different groups of things. The authors tested their approach on lots of real-world data and it did much better than other methods. |
Keywords
» Artificial intelligence » Alignment » Clustering » Encoder » Knowledge graph » Representation learning » Unsupervised » Vector space
