Papers by Mirza Mohtashim Alam
Knowledge Graph Embeddings using Neural Ito Process: From Multiple Walks to Stochastic Trajectories (2023.findings-acl)
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Mojtaba Nayyeri, Bo Xiong, Majid Mohammadi, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Jens Lehmann, Steffen Staab
| Challenge: | Existing knowledge graph embeddings have problems expressing knowledge graphs because they model a specific relation r from a head h to tails by transitioning deterministically to exactly one other point in the embeddable space. |
| Approach: | They propose a framework that models relations between nodes by relation-specific, stochastic transitions. |
| Outcome: | The proposed framework is expressive and generic subsuming state-of-the-art models operating on low-dimensional manifolds. |
Knowledge Graph Representation Learning using Ordinary Differential Equations (2021.emnlp-main)
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| Challenge: | Knowledge Graph Embeddings (KGEs) map entities and relations from knowledge graphs into a geometric space. |
| Approach: | They propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs) they represent each relation (edge) in a knowledge graph as a vector field on several manifolds. |
| Outcome: | The proposed model can preserve graph characteristics including structural aspects and semantics and avoid wrong inferences. |