Summary of Knowledge Graph Embeddings: a Comprehensive Survey on Capturing Relation Properties, by Guanglin Niu
Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties
by Guanglin Niu
First submitted to arxiv on: 16 Oct 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 Knowledge Graph Embedding (KGE) techniques are essential for transforming symbolic Knowledge Graphs (KGs) into numerical representations, enhancing deep learning models for knowledge-augmented applications. This paper focuses on the accurate modeling of relations in KGs, which carry semantic meaning and are crucial for KGE model performance. The authors summarize relation-aware mapping-based models, including those that utilize specific representation spaces, tensor decomposition, and neural networks. They also review models that capture various relation patterns like symmetry, asymmetry, inversion, and composition using modified tensor decomposition, relation-aware mappings, and rotation operations. Additionally, the paper introduces models that incorporate auxiliary information, hyperbolic spaces, and polar coordinates to capture implicit hierarchical relations among entities. Finally, the authors discuss potential future research directions, including integrating multimodal information, enhancing relation pattern modeling with rules, and developing models for dynamic KGE settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can better understand knowledge by converting it into a special kind of math problem that deep learning machines can solve. The main challenge is figuring out how to represent relationships between things in a way that makes sense. The authors review different ways that people have tried to do this, including using special spaces and operations to capture patterns in the relationships. They also explore new ideas for making these models more powerful and flexible. Overall, the goal is to create machines that can learn from vast amounts of knowledge and make better decisions as a result. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Knowledge graph
