Papers with Knowledge Graph Embedding
Thesis Proposal: Uncertainty in Knowledge Graph Embeddings (2025.naacl-srw)
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| Challenge: | Knowledge graph embedding (KGE) methods map entities and relations from knowledge graphs into numerical vector spaces. |
| Approach: | They propose to investigate various types of uncertainty in knowledge graph embedding methods and explore strategies to quantify, mitigate, and reason under uncertainty effectively. |
| Outcome: | The proposed methods have shown to be reliable in high-stakes domains and provide greater confidence in their use beyond benchmark datasets. |
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis (N18-2)
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| Challenge: | Existing sentiment analysis systems are prone to word shortening, exaggeration, lack of grammar and appropriate punctuation. |
| Approach: | They propose a two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis using the Knowledge Graph Embedding generated using the WordNet. |
| Outcome: | The proposed model outperforms the state-of-the-art system on the benchmark dataset of SemEval 2017 Task 5 by 1.7 and 3.7 points respectively. |
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
| Approach: | They propose a KGE framework with an automatic type embedding mechanism which can be easily integrated into any existing KGE model. |
| Outcome: | The proposed model can model and infer all the relation patterns and complex relations compared to state-of-the-art models on four datasets. |
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns (2021.acl-long)
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| Challenge: | Knowledge graph embeddings (KGE) models are increasingly deployed in domains with high stake decision making where it is critical to identify the potential security vulnerabilities that might cause failure. |
| Approach: | They propose to exploit the inductive abilities of knowledge graph embedding models by crafting adversarial additions that can improve model’s confidence on decoy facts. |
| Outcome: | The proposed attacks outperform state-of-the-art baselines on four KGE models for two publicly available datasets and generalize across all model-dataset combinations. |
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)
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| Challenge: | Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations. |
| Approach: | They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning . |
| Outcome: | The proposed method outperforms the state-of-the-art methods on unseen relation representations. |
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion (2022.naacl-main)
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| Challenge: | Existing methods to integrate text corpora with knowledge graphs (KGs) have been effective in various NLP tasks such as analyzing and predicting relationships between entities. |
| Approach: | They propose a method that borrows LDPs from entities that co-occur in sentences to represent entities that do not co-exist in a single sentence. |
| Outcome: | The proposed method improves the performance of prior methods such as TransE, DistMult, ComplEx and RotatE. |
Enhancing Hyperbolic Knowledge Graph Embeddings via Lorentz Transformations (2024.findings-acl)
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| Challenge: | Existing methods for knowledge graph embedding rely on tangent approximation and are not fully hyperbolic. |
| Approach: | They propose a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation. |
| Outcome: | The proposed method captures various types of relations including hierarchical structures. |
Croppable Knowledge Graph Embedding (2025.acl-long)
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| Challenge: | Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks. |
| Approach: | They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs. |
| Outcome: | The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity. |
Should We Use a Fixed Embedding Size? Customized Dimension Sizes for Knowledge Graph Embedding (2025.coling-main)
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| Challenge: | Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, which is crucial for knowledge completion, fusion, and inference. |
| Approach: | They propose to embed entities and relations into a low-dimensional space to enable knowledge Graphs to be effectively used by downstream AI tasks. |
| Outcome: | The proposed framework is universal and flexible, suitable for various KGE models. |
SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025.findings-emnlp)
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| Challenge: | Existing knowledge Graph Embedding approaches lack structural semantics of knowledge graphs . structure-aware calibration (SaCa) is a framework designed to calibrate KGEs based on global structural patterns. |
| Approach: | a new framework is designed to calibrate knowledge graphs using global structural patterns. |
| Outcome: | a new framework can calibrate KGE models using global structural patterns . the framework consistently boosts performance across ten models on link prediction and entity classification tasks . |