Knowledge Graphs
968 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
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.
Investigating Pretrained Language Models for Graph-to-Text Generation
UKPLab/plms-graph2text
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EMNLP (NLP4ConvAI) 2021
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
Next, we show that ATOMIC 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events.
Complex Query Answering with Neural Link Predictors
uclnlp/cqd
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ICLR 2021
Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.
A Generalization of Transformer Networks to Graphs
graphdeeplearning/graphtransformer
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This work closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs.
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.
VOGUE: Answer Verbalization through Multi-Task Learning
The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.
Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
CC1st/-step-by-step-mindspore
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Knowledge-Based Systems 2022
Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.
Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?
Large Language Models (LLMs) are advancing at a rapid pace, with significant improvements at natural language processing and coding tasks.
