Papers with knowledge graph embedding methods
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph. |
| Approach: | They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding . |
| Outcome: | The proposed method outperforms RotatE, Distmult and ComplEx on various data sets. |
RulE: Knowledge Graph Reasoning with Rule Embedding (2024.findings-acl)
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| Challenge: | Knowledge graph reasoning is an important problem for knowledge graphs. |
| Approach: | They propose a framework that leverages logical rules to enhance KG reasoning by learning rule embeddings from existing triplets and first-order rules. |
| Outcome: | The proposed framework outperforms existing embedding-based and rule-based methods on multiple benchmarks. |
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization (2020.emnlp-main)
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| Challenge: | Physical commonsense learning is an essential part of human-robot interaction . existing methods of learning physical commons sense suffer from generalization . |
| Approach: | They propose to use physical commonsense learning as a knowledge graph completion problem to better use latent relationships among training samples. |
| Outcome: | The proposed method outperforms existing methods in the human-robot interaction problem. |
VISTA: Visual-Textual Knowledge Graph Representation Learning (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods only consider the structure of a knowledge graph, but some recent proposed methods utilize images or text descriptions of entities in a VTKG. |
| Approach: | They propose a visual-textual knowledge graph (VTKG) where triplets can be explained using images and entities and relations can accompany text descriptions. |
| Outcome: | The proposed method outperforms state-of-the-art knowledge graph completion methods in real-world knowledge graphs. |