no code implementations • EMNLP 2021 • Mojtaba Nayyeri, Chengjin Xu, Franca Hoffmann, Mirza Mohtashim Alam, Jens Lehmann, Sahar Vahdati
Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models.
no code implementations • 6 Apr 2023 • Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal Welke, Michael Martin, Jens Lehmann, Sahar Vahdati
Skilled employees are the most important pillars of an organization.
no code implementations • 15 Aug 2022 • Mojtaba Moattari, Sahar Vahdati, Farhana Zulkernine
Experimental results on benchmark Knowledge Graphs (KGs) such as FB15K and WN18 show that the proposed approach outperforms the state-of-the-art models in entity prediction task using linear and bilinear methods and other recent powerful ones.
no code implementations • 9 Mar 2022 • Md Rashad Al Hasan Rony, Mirza Mohtashim Alam, Semab Ali, Jens Lehmann, Sahar Vahdati
The learning process of such models can be performed by contrasting positive and negative triples.
no code implementations • 3 Jul 2021 • Mojtaba Nayyeri, Gokce Muge Cil, Sahar Vahdati, Francesco Osborne, Mahfuzur Rahman, Simone Angioni, Angelo Salatino, Diego Reforgiato Recupero, Nadezhda Vassilyeva, Enrico Motta, Jens Lehmann
This is typical for KGs that categorize a large number of entities (e. g., research articles, patents, persons) according to a relatively small set of categories.
no code implementations • 11 Apr 2021 • Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann
For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same.
no code implementations • 13 Oct 2020 • Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann, Sahar Vahdati
To this end, we represent each relation (edge) in a KG as a vector field on a smooth Riemannian manifold.
no code implementations • 8 Jun 2020 • Mojtaba Nayyeri, Sahar Vahdati, Can Aykul, Jens Lehmann
Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs.
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 • 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.