Summary of Arbitrary Time Information Modeling Via Polynomial Approximation For Temporal Knowledge Graph Embedding, by Zhiyu Fang et al.
Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding
by Zhiyu Fang, Jingyan Qin, Xiaobin Zhu, Chun Yang, Xu-Cheng Yin
First submitted to arxiv on: 1 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper introduces a novel approach to Temporal Knowledge Graphs (TKGs), called PTBox, which tackles two significant challenges in TKG modeling. The first challenge is the limited ability to model arbitrary timestamps continuously, and the second is the lack of rich inference patterns under temporal constraints. To address these issues, the authors propose a method that decomposes time information using polynomials and represents entities as hyperrectangle boxes with box embeddings. This allows for flexible representation of arbitrary timestamps and robust learning of entity representations. Theoretical analysis shows that PTBox can encode arbitrary time information, even unseen timestamps, while capturing rich inference patterns and higher-arity relations in the knowledge base. Experimental results on real-world datasets demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make sense of temporal data, which is important for understanding how things change over time. The problem is that current methods are not very good at dealing with arbitrary timestamps or complex relationships between entities. To solve these problems, the authors propose a new method called PTBox, which breaks down time information into smaller pieces and represents entities as simple shapes. This allows their model to learn more robust representations of entities and make better predictions about what might happen in the future. The results show that this approach is very effective at understanding complex temporal data. |
Keywords
» Artificial intelligence » Inference » Knowledge base
