Knowledge Graph Embeddings

Outline

  • Why embed the KG ?
  • Notations
  • General Algorithm
  • Scoring Function
    • Translation Based
    • Semantic Matching Based
  • KG embedding with additional resources
    • Entity types
    • Relation Paths
    • Textual Descriptions
  • Conclusion

Outline

  • Why embed the KG ?
  • Notations
  • General Algorithm
  • Scoring Function
    • Translation Based
    • Semantic Matching Based
  • KG embedding with additional resources
    • Entity types
    • Relation Paths
    • Textual Descriptions
  • Conclusion
  • Embedding the KG into a continuous space while preserving the properties and semantics of the whole KG.
  • Applications
    • Link Prediction: 
    • Triple Classification:
    • Entity Resolution:
    • Relation Extraction:
    • Question Answering:

Why Embed the KG ?

Outline

  • Why embed the KG ?
  • Notations
  • General Algorithm
  • Scoring Function
    • Translation Based
    • Semantic Matching Based
  • KG embedding with additional resources
    • Entity types
    • Relation Paths
    • Textual Descriptions
  • Conclusion

Notations

  • KG contains entities \(e \in \mathbb{E} \) and relationships \( r \in \mathbb{R} \)
  • Each KB triple (fact) is represented by \( (h,r,t)\)
  • \( h\): head entity, \(t\): tail entity and \(r\): relation
  • Example:  (AlfredHitchcock, DirectorOf, Psycho)
  • Scoring function \(f_r(h, t) \) : measures the plausibility of the fact \((h,r,t)\)

Outline

  • Why embed the KG ?
  • Notations
  • General Algorithm
  • Scoring Function
    • Translation Based
    • Semantic Matching Based
  • KG embedding with additional resources
    • Entity types
    • Relation Paths
    • Textual Descriptions
  • Conclusion

General Algorithm

Outline

  • Why embed the KG ?
  • Notations
  • General Algorithm
  • Scoring Function
    • Translation Based
    • Semantic Matching Based
  • KG embedding with additional resources
    • Entity types
    • Relation Paths
    • Textual Descriptions
  • Conclusion

Translation Based Approaches - TransE

  • Head entity \(\mathbf{h}\) and tail entity \( \mathbf{t}\) of a fact \((h,r,t)\) are vectors in \(\mathbb{R}^d\)
  • Relation \(\mathbf{r}\) is a translation in \(\mathbb{R}^d\)
  • Scoring function: \(f_r(h,t) = -\lVert \mathbf{h}+\mathbf{r}-\mathbf{t}\lVert_{1/2}\)  
  • Ranking Loss: \( \mathcal{L} = \) 
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