Knowledge Graphs
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Subtasks
Greatest papers with code
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).
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
Learning Deep Generative Models of Graphs
ICLR 2018
• JiaxuanYou/graph-generation
•
Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry.
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
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
We study the problem of learning to reason in large scale knowledge graphs (KGs).
KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS