# Knowledge Graph Embedding

189 papers with code • 1 benchmarks • 2 datasets

## 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

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

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

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

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

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.