Summary of From Semantics to Hierarchy: a Hybrid Euclidean-tangent-hyperbolic Space Model For Temporal Knowledge Graph Reasoning, by Siling Feng et al.
From Semantics to Hierarchy: A Hybrid Euclidean-Tangent-Hyperbolic Space Model for Temporal Knowledge Graph Reasoning
by Siling Feng, Zhisheng Qi, Cong Lin
First submitted to arxiv on: 30 Aug 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 This research paper proposes a novel hybrid geometric space approach to improve temporal knowledge graph (TKG) reasoning, which predicts future events based on historical data. The authors address the limitations of existing Euclidean and hyperbolic models by leveraging their strengths in a multi-space parameter modeling framework. The method begins by capturing complex semantics through fact co-occurrence and autoregressive methods in Euclidean space, followed by transformations into Tangent and Hyperbolic spaces. A hybrid inductive bias is achieved by combining scoring functions from hyperbolic and Euclidean spaces using a learnable query-specific mixing coefficient. Experimental results on four TKG benchmarks demonstrate the effectiveness of this approach, reducing error by up to 15.0% in mean reciprocal rank on YAGO compared to previous single-space models. The authors’ approach also shows adaptive capabilities for datasets with varying levels of semantic and hierarchical complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to improve a way computers can predict what will happen in the future based on things that have happened before. It’s hard because there are many different kinds of information involved, like words and relationships between them. The authors make a new approach that combines two old ways of doing this: one that does well with complex meanings but not with hierarchical structures, and another that does well with hierarchical structures but not with complex meanings. They test their new method on four sets of data and show it works better than the old methods. This is important because it could help computers make better predictions in the future. |
Keywords
» Artificial intelligence » Autoregressive » Knowledge graph » Semantics
