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KBGAN: Adversarial Learning for Knowledge Graph Embeddings
AbstractWe introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling …...
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
AbstractIn this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix …...
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
AbstractDistance-based knowledge graph embeddings have shown substantial improvement on the knowledge graph link prediction task, from TransE to the latest state-of-the-art RotatE. However, complex relations such as N-to-1, 1-to-N and …...
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding
AbstractIn knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated. This makes it difficult to fairly compare the results of …...
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
AbstractLearning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts …...