Knowledge Graph Embedding

196 papers with code • 1 benchmarks • 4 datasets

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Libraries

Use these libraries to find Knowledge Graph Embedding models and implementations

Most implemented papers

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

DeepGraphLearning/KnowledgeGraphEmbedding ICLR 2019

We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.

Inductive Relation Prediction by Subgraph Reasoning

kkteru/grail ICML 2020

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

MIRALab-USTC/KGE-HAKE 21 Nov 2019

HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

yzhangee/NSCaching 16 Dec 2018

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 2 Jul 2019

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 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 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 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

tranhungnghiep/meim-kge 30 Sep 2022

Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

cai-lw/KBGAN NAACL 2018

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.