Search Results for author: Hamed Shariat Yazdi

Found 8 papers, 3 papers with code

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

1 code implementation18 Nov 2019 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.

Knowledge Graph Completion Knowledge Graph Embedding +5

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

2 code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.

Knowledge Graph Embedding Link Prediction +1

MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs

2 code implementations25 May 2019 Afshin Sadeghi, Damien Graux, Hamed Shariat Yazdi, Jens Lehmann

We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms.

Link Prediction Relational Pattern Learning +1

Soft Marginal TransE for Scholarly Knowledge Graph Completion

no code implementations27 Apr 2019 Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann, Hamed Shariat Yazdi

In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata.

Link Prediction Question Answering

Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function

no code implementations9 Jul 2019 Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati, Hamed Shariat Yazdi, Jens Lehmann

To tackle this problem, several loss functions have been proposed recently by adding upper bounds and lower bounds to the scores of positive and negative samples.

Knowledge Graph Embeddings Knowledge Graphs +2

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