Complex Embeddings for Simple Link Prediction

Code

• github.com/ttrouill/complex
  OfficialIn papernone★ 0
• github.com/Accenture/AmpliGraph
  tf★ 2,231
• github.com/pykeen/pykeen
  pytorch★ 1,963
• github.com/dayanayuan/raa-kgc
  pytorch★ 38
• github.com/sntcristian/and-kge
  pytorch★ 21
• github.com/iesl/geometric_graph_embedding
  pytorch★ 16
• github.com/stellargraph/stellargraph
  tf★ 0
• github.com/pyg-team/pytorch_geometric/blob/2947af7733d87315a9218a0bfabbfda10b595f0e/torch_geometric/nn/kge/complex.py
  pytorch★ 0
• github.com/bi-graph/emgraph
  tf★ 0

Abstract

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.

Tasks

Benchmark Results

Reproductions