However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs.
Ranked #1 on Link Prediction on YAGO3-10
In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.
Ranked #1 on Link Prediction on WN18
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on Link Prediction on WN18 (training time (s) metric)
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).
Ranked #1 on Knowledge Graph Completion on FB15k-237 (Hits@1 metric)
This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.
Ranked #11 on Link Prediction on WN18
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.