Learning to Represent Knowledge Graphs with Gaussian Embedding

@article{He2015LearningTR,
  title={Learning to Represent Knowledge Graphs with Gaussian Embedding},
  author={Shizhu He and Kang Liu and Guoliang Ji and Jun Zhao},
  journal={Proceedings of the 24th ACM International on Conference on Information and Knowledge Management},
  year={2015}
}
  • Shizhu He, Kang Liu, +1 author Jun Zhao
  • Published 2015
  • Computer Science
  • Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
  • The representation of a knowledge graph (KG) in a latent space recently has attracted more and more attention. [...] Key Method Each entity/relation is represented by a Gaussian distribution, where the mean denotes its position and the covariance (currently with diagonal covariance) can properly represent its certainty.Expand Abstract
    190 Citations
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    • 5
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    • 111
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    • 37
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    Knowledge Graph Embedding: A Survey of Approaches and Applications
    • 633
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    References

    SHOWING 1-10 OF 11 REFERENCES
    Knowledge Graph Embedding by Translating on Hyperplanes
    • 1,277
    • Highly Influential
    Learning Entity and Relation Embeddings for Knowledge Graph Completion
    • 1,317
    • Highly Influential
    • PDF
    Translating Embeddings for Modeling Multi-relational Data
    • 2,554
    • Highly Influential
    • PDF
    Reasoning With Neural Tensor Networks for Knowledge Base Completion
    • 1,285
    • Highly Influential
    • PDF
    A latent factor model for highly multi-relational data
    • 310
    • Highly Influential
    • PDF
    Learning Structured Embeddings of Knowledge Bases
    • 657
    • Highly Influential
    • PDF
    A semantic matching energy function for learning with multi-relational data
    • 410
    • Highly Influential
    • PDF
    Learning Distributed Representations of Concepts Using Linear Relational Embedding
    • 93
    • Highly Influential
    • PDF
    A Three-Way Model for Collective Learning on Multi-Relational Data
    • 1,053
    • Highly Influential
    • PDF
    Freebase: a collaboratively created graph database for structuring human knowledge
    • 3,046
    • Highly Influential