Papers by Hamed Shariat Yazdi

1 papers
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation (2020.coling-main)

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Challenge: Existing knowledge graphs that contain time information for entities and relations have been used for learning and inference.
Approach: They propose a temporal evolution of entity embedding that defines the temporal rotation from the initial time to the current time in the complex vector space.
Outcome: The proposed model outperforms existing state-of-the-art models for link prediction on three different TKGs.

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