Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction

@inproceedings{Gardner2015EfficientAE,
  title={Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction},
  author={Matt Gardner and Tom Michael Mitchell},
  booktitle={EMNLP},
  year={2015}
}
  • Matt Gardner, Tom Michael Mitchell
  • Published in EMNLP 2015
  • Computer Science
  • We explore some of the practicalities of using random walk inference methods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. [...] Key Method In addition to being conceptually simpler than PRA, SFE is much more efficient, reducing computation by an order of magnitude, and more expressive, allowing for much richer features than paths between two nodes in a graph. We show experimentally that this technique gives substantially better performance than PRA and its variants…Expand Abstract
    Title Knowledge base completion using distinct subgraph paths
    Knowledge base completion using distinct subgraph paths
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    Context-aware Path Ranking for Knowledge Base Completion
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    Efficient Rule Learning with Template Saturation for Knowledge Graph Completion
    Using Patterns in Knowledge Graphs for Targeted Information Extraction
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    • Highly Influenced
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