• Corpus ID: 202763266

Quaternion Knowledge Graph Embeddings

@inproceedings{Zhang2019QuaternionKG,
  title={Quaternion Knowledge Graph Embeddings},
  author={Shuai Zhang and Yi Tay and Lina Yao and Qi Liu},
  booktitle={Neural Information Processing Systems},
  year={2019}
}
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings. More specifically, quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, are utilized to represent entities. Relations are modelled as rotations in the quaternion space. The advantages of the proposed approach are: (1) Latent inter-dependencies (between all components… 
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245 Citations
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Background Citations
119
Methods Citations
117
Results Citations
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245 Citations

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References

SHOWING 1-10 OF 35 REFERENCES

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Experimental results show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.

TorusE: Knowledge Graph Embedding on a Lie Group

A novel embedding model, TorusE, is proposed that outperforms other state-of-the-art approaches such as TransE, DistMult and ComplEx on a standard link prediction task and is scalable to large-size knowledge graphs and is faster than the original TransE.

Knowledge Graph Embedding by Translating on Hyperplanes

This paper proposes TransH which models a relation as a hyperplane together with a translation operation on it and can well preserve the above mapping properties of relations with almost the same model complexity of TransE.

Complex and Holographic Embeddings of Knowledge Graphs: A Comparison

This short paper provides a comparison of two state-of-the-art knowledge graph embeddings for which their equivalence has recently been established, i.e., ComplEx and HolE.

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication.

Holographic Embeddings of Knowledge Graphs

Holographic embeddings (HolE) are proposed to learn compositional vector space representations of entire knowledge graphs to outperform state-of-the-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.

SimplE Embedding for Link Prediction in Knowledge Graphs

It is proved SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity and shown empirically that, despite its simplicity, SimplE outperforms several state-of-the-art tensor factorization techniques.

Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network

A new kind of additional information, called entity neighbors, are proposed, which contain both semantic and topological features about given entity, and a deep memory network model is developed to encode information from neighbors.

Translating Embeddings for Modeling Multi-relational Data

TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.

Learning Entity and Relation Embeddings for Knowledge Graph Completion

TransR is proposed to build entity and relation embeddings in separate entity space and relation spaces by first projecting entities from entity space to corresponding relation space and then building translations between projected entities.