Knowledge Graph Embedding
189 papers with code • 1 benchmarks • 2 datasets
Libraries
Use these libraries to find Knowledge Graph Embedding models and implementations
FB15k
KG20C
Most implemented papers
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
DeepGraphLearning/KnowledgeGraphEmbedding
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ICLR 2019
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding.
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
Erik-BM/NIVAUC
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A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.
Composition-based Multi-Relational Graph Convolutional Networks
malllabiisc/CompGCN
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ICLR 2020
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
AutoML-4Paradigm/Interstellar
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NeurIPS 2020
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.
Multi-Relational Embedding for Knowledge Graph Representation and Analysis
tranhungnghiep/AnalyzeKGE
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PhD Dissertation, The Graduate University for Advanced Studies, SOKENDAI, Japan 2020
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.
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
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.
