Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis (2021.naacl-main)
Copied to clipboard
| 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. |
Similar Papers
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph embeddings (KGs) are limited in their flexibility and difficulties in generalizing them for higher-dimensional rotations. |
| Approach: | They propose a KGE model employing matrices for entities and block-diagonal orthogonal matrics with Riemannian optimization for relations that captures several relation patterns that rotation-based methods can identify. |
| Outcome: | The proposed model outperforms state-of-the-art models while reducing the number of relation parameters. |
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples . |
| Approach: | They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE. |
| Outcome: | The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR . |
Knowledge Graph Embedding Compression (2020.acl-main)
Copied to clipboard
| Challenge: | Knowledge graph (KG) embedding techniques that learn continuous embedds of entities and relations consume a large amount of storage and memory. |
| Approach: | They propose a method that compresses the KG embedding layer by representing each entity in the KA as a vector of discrete codes and then composes the embeddables from these codes. |
| Outcome: | The proposed approach achieves 50-1000x compression of embeddings with a minor loss in performance on standard KG embeddable evaluations and retains the ability to perform reasoning tasks such as KG inference. |
Improving Knowledge Graph Embedding Using Simple Constraints (P18-1)
Copied to clipboard
| Challenge: | Recent efforts focused on designing more complicated models or incorporating extra information beyond triples. |
| Approach: | They propose to use non-negativity constraints on entity representations and approximate entailment constraints on relation representations to improve KG embedding. |
| Outcome: | The proposed model outperforms baseline models on WordNet, Freebase, and DBpedia. |
Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
| Approach: | They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs . |
| Outcome: | The proposed approach can extend existing KGE models, and validates against baselines. |
Efficient Entity Embedding Construction from Type Knowledge for BERT (2022.findings-aacl)
Copied to clipboard
| Challenge: | Existing work has shown advantages of incorporating knowledge graphs (KGs) into BERT for various NLP tasks. |
| Approach: | They propose to integrate knowledge graphs into BERT to train entity embeddings to include rich information of factual knowledge. |
| Outcome: | The proposed models perform very well when combined with context. |
Croppable Knowledge Graph Embedding (2025.acl-long)
Copied to clipboard
| 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. |
Knowledge Graph Alignment with Entity-Pair Embedding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Knowledge Graph (KG) alignment are not satisfactory. |
| Approach: | They propose a method that directly learns embeddings of entity-pairs for KG alignment. |
| Outcome: | The proposed approach can achieve state-of-the-art on five real-world datasets. |
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge graph embedding (KGE) is a computational approach to learn continuous vector representations of relations and entities in knowledge graphs. |
| Approach: | They propose a transition-based method to learn continuous vector representations of relations and entities in knowledge graph (KG) it replaces a single relation vector in the relation part with a synthetic relation representation with entity-relation interactions to solve these problems. |
| Outcome: | The proposed method achieves state-of-the-art on a large knowledge graph dataset. |
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |