Summary of Transformer-based Reasoning For Learning Evolutionary Chain Of Events on Temporal Knowledge Graph, by Zhiyu Fang et al.
Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph
by Zhiyu Fang, Shuai-Long Lei, Xiaobin Zhu, Chun Yang, Shi-Xue Zhang, Xu-Cheng Yin, Jingyan Qin
First submitted to arxiv on: 1 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper proposes a novel Transformer-based reasoning model, ECEformer, for Temporal Knowledge Graph (TKG) reasoning. Existing methods can learn embeddings for factual elements in quadruples by integrating temporal information but often fail to infer the evolution of temporal facts. The proposed method addresses this limitation by exploring internal structure and semantic relationships within individual quadruples and learning a unified representation of contextual and temporal correlations among different quadruples. The model unfolds neighborhood subgraphs into an evolutionary chain of events, using a Transformer encoder to learn intra-quadruple embeddings. A mixed-context reasoning module based on the multi-layer perceptron (MLP) learns inter-quadruple representations while accomplishing temporal knowledge reasoning. To enhance timeliness, an additional time prediction task is devised. The method achieves state-of-the-art performance and effectiveness on six benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at understanding time-based information. Currently, computers can learn about individual facts but struggle to understand how these facts change over time. To solve this problem, the researchers propose a new way of processing information called ECEformer. This method looks at the relationships between different facts and learns to represent them in a more meaningful way. The goal is to make computers better at completing missing information along a timeline. The researchers test their method on six different datasets and find that it performs much better than existing methods. |
Keywords
» Artificial intelligence » Encoder » Knowledge graph » Transformer
