Knowledge Graphs

365 papers with code • 2 benchmarks • 29 datasets

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Greatest papers with code

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

dmlc/dgl 11 Apr 2019

The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.

Knowledge Graphs Link Prediction

OpenKE: An Open Toolkit for Knowledge Embedding

thunlp/OpenKE EMNLP 2018

We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.

Information Retrieval Knowledge Graphs +2

Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes

Accenture/AmpliGraph 18 May 2021

Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks.

Knowledge Graph Embedding Knowledge Graphs

Knowledge Graph Completion via Complex Tensor Factorization

Accenture/AmpliGraph 22 Feb 2017

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.

Knowledge Graph Completion Link Prediction +1

Holographic Embeddings of Knowledge Graphs

Accenture/AmpliGraph 16 Oct 2015

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.

Knowledge Graphs Link Prediction +1

Modeling Relational Data with Graph Convolutional Networks

tkipf/gae 17 Mar 2017

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

General Classification Graph Classification +5

ERNIE: Enhanced Language Representation with Informative Entities

thunlp/ERNIE ACL 2019

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.

Entity Linking Entity Typing +5

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

snap-stanford/ogb 17 Mar 2021

We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.

Graph Learning Graph Regression +3

Open Graph Benchmark: Datasets for Machine Learning on Graphs

snap-stanford/ogb NeurIPS 2020

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.

Knowledge Graphs