Knowledge Graph Completion
206 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
Latest papers with no code
Knowledge Graph Assisted Automatic Sports News Writing
In this paper, we present a novel method for automatically generating sports news, which employs a unique algorithm that extracts pivotal moments from live text broadcasts and uses them to create an initial draft of the news.
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility.
Rendering Graphs for Graph Reasoning in Multimodal Large Language Models
In this paper, we take the first step in incorporating visual information into graph reasoning tasks and propose a new benchmark GITQA, where each sample is a tuple (graph, image, textual description).
Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
First, we empirically find and theoretically motivate why sampling uniformly at random vastly overestimates the ranking performance of a method.
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
Firstly, we introduce a lightweight distributed knowledge graph completion architecture that utilizes knowledge graph embedding for data analysis.
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural knowledge.
Path-based Explanation for Knowledge Graph Completion
Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years.
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion
As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing.
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings into multiple internal convolution kernels to improve the external convolution kernels of the traditional convolutional embedding model.
Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.
