Knowledge Graph Embedding
189 papers with code • 1 benchmarks • 2 datasets
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding.
A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths.
The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs.
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.
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.