Papers with KGEs
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding (2021.eacl-main)
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
| Approach: | They extend the random walk model of word embeddings to Knowledge Graph Embeddings (KGEs) they propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
| Outcome: | The proposed learning objective is motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
Counterfactual Reasoning with Knowledge Graph Embeddings (2024.eacl-long)
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| Challenge: | Knowledge graph embeddings were originally developed to infer true but missing facts in incomplete knowledge repositories. |
| Approach: | They propose a task that requires models to reason on a counterfactual KG. |
| Outcome: | The proposed task connects knowledge graph completion and counterfactual reasoning. |
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis (2021.naacl-main)
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| Challenge: | Knowledge Graph Embeddings (KGEs) have been explored in recent years due to their promise for a wide range of applications. |
| Approach: | They propose a KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches. |
| Outcome: | The proposed framework reduces the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches while producing competitive performance. |
Sequence-to-Sequence Knowledge Graph Completion and Question Answering (2022.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embeddable vectors. |
| Approach: | They propose to use an off-the-shelf encoder-decoder Transformer model to generate a knowledge graph embedding model that can be used for KG link prediction and incomplete KG question answering. |
| Outcome: | The proposed model outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning. |
BiQUE: Biquaternionic Embeddings of Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing knowledge graph embeddings rely on geometric operations to model relational patterns such as symmetry and hierarchical semantics. |
| Approach: | They propose a new knowledge graph embedding model that integrates multiple geometric transformations to model multi-relational knowledge graphs. |
| Outcome: | Experiments on five datasets show that BiQUE can model symmetry, inversion, and composition. |
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. |
Knowledge GeoGebra: Leveraging Geometry of Relation Embeddings in Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Knowledge graph embedding models are limited to the algebra and geometry of the entity embeddable space, the algebra of the relation embeddible space, and the interaction between relation and entity embeds. |
| Approach: | They propose a method that leverages the geometry of relation embeddings and generalizes it with the concept of a butterfly curve, consecutively. |
| Outcome: | The proposed model outperforms existing models on the WN18RR, FB15K-237 and YouTube benchmarks. |
KGE Calibrator: An Efficient Probability Calibration Method of Knowledge Graph Embedding Models for Trustworthy Link Prediction (2025.emnlp-main)
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| Challenge: | Existing methods for probability calibration of knowledge graph embedding models are ill-suited for KGEs. |
| Approach: | They propose a method to calibrate knowledge graph embedding models for ranking-based link prediction using a Jump Selection Strategy and Multi-Binning Scaling to enhance reliability. |
| Outcome: | Experiments show that the KGEC outperforms existing calibration methods in terms of effectiveness and efficiency. |