Knowledge Graph Completion
208 papers with code • 7 benchmarks • 16 datasets
Knowledge graphs $G$ are represented as a collection of triples $\{(h, r, t)\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
Benchmarks
These leaderboards are used to track progress in Knowledge Graph Completion
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Libraries
Use these libraries to find Knowledge Graph Completion models and implementationsDatasets
MovieLens
FB15k
WN18
InferWiki
CoDEx Medium
CoDEx Large
Subtasks
Most implemented papers
A Re-evaluation of Knowledge Graph Completion Methods
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs.
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
soledad921/ATISE
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Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Panda0406/Zero-shot-knowledge-graph-relational-learning
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Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Knowledge graph embedding methods perform this task by representing entities and relations as embedding vectors and modeling their interactions to compute the matching score of each triple.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
FedE: Embedding Knowledge Graphs in Federated Setting
AnselCmy/FedE
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Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
wjk666/disenkgat
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Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks.
A Probabilistic Framework for Knowledge Graph Data Augmentation
anongekc/gekcs
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We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the learning process of neural link predictors.
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
zjunlp/promptkg
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Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information.
