Unified Framework

  1. Analogical inference for multi-relational embeddings
  2. Relational inductive biases, deep learning, and graph networks
  3. Towards understanding the geometry of knowledge graph embeddings
  4. Neural knowledge acquisition via mutual attention between knowledge graph and text
  5. On multi-relational link prediction with bilinear models
  6. On the equivalence of holographic and complex embeddings for link prediction
  7. Link Prediction on a Heterogeneous Knowledge Base

Interpretability

  1. Interaction embeddings for prediction and explanation in knowledge graphs
  2. An interpretable knowledge transfer model for knowledge base completion

Knowledge Aggregation

  1. Language models as knowledge bases?

Scalability

  1. Efficient probabilistic logic reasoning with graph neural networks
  2. Probabilistic logic neural networks for reasoning
  3. Differentiable learning of logical rules for knowledge base reasoning
  4. Holographic embeddings of knowledge graphs
Ryan

Ryan

Data Scientist

Leave a Reply