no code implementations • 27 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.
2 code implementations • 25 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.
Ranked #7 on Link Prediction on FB15k
no code implementations • 9 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.
no code implementations • 20 Aug 2019 • Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann, Hamed Shariat Yazdi
We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph.
Ranked #17 on Link Prediction on FB15k
no code implementations • 2 Sep 2019 • Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh, Hamed Shariat Yazdi, Jens Lehmann
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
no code implementations • 25 Sep 2019 • Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh, Hamed Shariat Yazdi, Jens Lehmann
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
2 code implementations • 18 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.
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.