Summary of Learning Granularity Representation For Temporal Knowledge Graph Completion, by Jinchuan Zhang et al.
Learning Granularity Representation for Temporal Knowledge Graph Completion
by Jinchuan Zhang, Tianqi Wan, Chong Mu, Guangxi Lu, Ling Tian
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Learning Granularity Representation (LGRe) method addresses the incompleteness issue in Temporal Knowledge Graphs (TKGs). By incorporating temporal information, TKGs reflect dynamic structural knowledge and evolutionary patterns. Existing methods overlook the impact of history from a multi-granularity aspect. LGRe consists of two components: Granularity Representation Learning (GRL), which employs time-specific convolutional neural networks to capture interactions between entities and relations at different granularities, and Adaptive Granularity Balancing (AGB), which generates adaptive weights for these embeddings according to temporal semantics. A temporal loss function is introduced to reflect similar semantics of adjacent timestamps. The effectiveness of LGRe is demonstrated through extensive experimental results on four event benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make Temporal Knowledge Graphs more complete. TKGs are special kinds of networks that show how things are related over time. Right now, they’re limited because they don’t take into account the history and patterns in those relationships. The authors propose a new method called LGRe that learns how to represent these relationships at different levels of detail. This helps the graph be more complete and accurate. They tested this method on four different datasets and showed it works well. |
Keywords
» Artificial intelligence » Loss function » Representation learning » Semantics
