Jan 5, 2021 · In this paper, we propose a novel knowledge graph embedding model called TimE, which presents each entry of the head (or tail) entity embeddings ...
Jan 5, 2021 · In this paper, we propose a novel knowledge graph embedding model called TimE, which presents each entry of the head (or tail) entity embeddings ...
Knowledge graph embedding, which aims to learn distributed representations of entities and relations, has been proven to be an effective method for ...
Semantic Scholar extracted view of "Knowledge graph embedding by translating in time domain space for link prediction" by Qianjin Zhang et al.
These models interpret each relation as a translation in the latent space: the relation embedding is just added to the head embedding, and we expect to land ...
"Knowledge graph embedding by translating in time domain space for link prediction". ... relation rotational knowledge graph embedding for link prediction ...
KGE models embed entities and relations into a low-dimensional vector space while preserving the structure of the KG and its underlying semantic information.
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Knowledge graph embedding, which aims to represent entities and relations of a knowledge graph as low dimensional vectors in a continuous vector space, has ...
Jun 5, 2024 · Knowledge graph embedding by translating in time domain space for link prediction. Knowl.-Based Syst. 2021;212:106564. doi: 10.1016/j.knosys ...
In order to automatically predict missing triples, researchers have proposed many knowledge graph embedding models for link prediction task. However, most ...