Knowledge Graph Embeddings
109 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
Use these libraries to find Knowledge Graph Embeddings models and implementations
MutualFriends
OLPBENCH
Most implemented papers
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.
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.
Multi-Relational Embedding for Knowledge Graph Representation and Analysis
tranhungnghiep/AnalyzeKGE
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PhD Dissertation, The Graduate University for Advanced Studies, SOKENDAI, Japan 2020
The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs.
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.
Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
YueLiu/NeuralTripleTranslation
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Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings.
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).
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
tensorflow/neural-structured-learning
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ACL 2020
However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs.
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
zjunlp/relphormer
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EMNLP 2021
Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block.
Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
Furthermore, we have implemented a fine-tuning architecture that adapts the knowledge graph embeddings to the effect prediction task and leads to better performance.
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Knowledge graph completion aims to perform link prediction between entities.
