Knowledge Graphs
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Subtasks
Greatest papers with code
Benchmarking Graph Neural Networks
• dmlc/dgl
• 
In this paper, we propose a reproducible GNN benchmarking framework, with the facility for researchers to add new datasets and models conveniently.
OpenKE: An Open Toolkit for Knowledge Embedding
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 QUESTION ANSWERING REPRESENTATION LEARNING
Knowledge Graph Completion via Complex Tensor Factorization
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.
SOTA for Knowledge Graphs on FB15k
KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.
Modeling Relational Data with Graph Convolutional Networks
• tkipf/gae
• 
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
SOTA for Node Classification on AIFB
GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION NODE CLASSIFICATION
ERNIE: Enhanced Language Representation with Informative Entities
ACL 2019
• thunlp/ERNIE
•
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.
SOTA for Relation Extraction on FewRel
ENTITY TYPING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE SENTIMENT ANALYSIS
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
CVPR 2018
• JudyYe/zero-shot-gcn
•
Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category).
Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning
• DeepGraphLearning/RecommenderSystems
•
Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations.
KNOWLEDGE GRAPHS POLICY GRADIENT METHODS RECOMMENDATION SYSTEMS
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
ICLR 2019
• DeepGraphLearning/KnowledgeGraphEmbedding
•
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
#4 best model for
Link Prediction
on WN18
Convolutional 2D Knowledge Graph Embeddings
In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.
SOTA for Link Prediction on WN18