Knowledge Graphs
955 papers with code • 3 benchmarks • 41 datasets
Benchmarks
These leaderboards are used to track progress in Knowledge Graphs
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Libraries
Use these libraries to find Knowledge Graphs models and implementationsDatasets
ConceptNet
FB15k
ATOMIC
CORD-19
ReDial
Semantic Scholar
MetaQA
DBP15K
ComplexWebQuestions
Jericho
Subtasks
Most implemented papers
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.
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
JudyYe/zero-shot-gcn
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CVPR 2018
Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category).
Multi-Hop Knowledge Graph Reasoning with Reward Shaping
salesforce/MultiHopKG
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EMNLP 2018
Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs).
Learning Sequence Encoders for Temporal Knowledge Graph Completion
In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations.
No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph
Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction.
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
hwwang55/MKR
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Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
Text Generation from Knowledge Graphs with Graph Transformers
rikdz/GraphWriter
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NAACL 2019
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
wuliwei9278/SSE-PT
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NeurIPS 2019
We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples.
