ATAP: Automatic Template-Augmented Commonsense Knowledge Graph Completion via Pre-Trained Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Commosense knowledge graphs (CKGC) are powerful representations of real-world commonsense knowledge. |
| Approach: | They propose a framework that uses automatically generated prompt templates combined with pre-trained language models to improve CKGC performance. |
| Outcome: | The proposed framework mitigates the long-tail problem and improves CKGC performance on a large dataset. |
Similar Papers
Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to augment knowledge graph completion require factual triples or manual prompts to extract knowledge from a pre-trained language model. |
| Approach: | They propose a tool that generates quality query prompts and retrieves support information from large text corpora to probe knowledge from a pre-trained language model. |
| Outcome: | The proposed method outperforms embedding-based, graph-based and PLM-based methods on two benchmark datasets. |
Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an ability to generate unobvious concepts. |
| Approach: | They propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-level relationships between concepts. |
| Outcome: | The proposed framework can capture high-order relationships between concepts in four special cases: long path, path-to-path, router, and graph-node-path. |
Path-enhanced Pre-trained Language Model for Knowledge Graph Completion (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have achieved remarkable knowledge graph completion (KGC) success. |
| Approach: | They propose a path-enhanced pre-trained language model-based knowledge graph completion method which uses multi-view generation to infer missing facts in triple-level and path-level simultaneously. |
| Outcome: | The proposed method significantly improves the performance of the knowledge graph completion task. |
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (2022.coling-1)
Copied to clipboard
| Challenge: | Existing knowledge graph completion models require only a few associative triples to complete a relationship. |
| Approach: | They propose to perform data augmentation from two perspectives to solve the FKGC problem by inferring new triple facts from existing models. |
| Outcome: | The proposed framework can be applied to a number of existing models. |
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph completion methods struggle with long-tail entities due to limited structural information and imbalanced distributions of entities. |
| Approach: | They propose a framework that integrates a large language model and a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead. |
| Outcome: | The proposed model reduces training overhead and finetuning costs on benchmark datasets. |
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential . |
| Approach: | a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information. |
| Outcome: | The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules. |
CN-AutoMIC: Distilling Chinese Commonsense Knowledge from Pretrained Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing commonsense knowledge graphs are limited to English, hindering research in non-English languages. |
| Approach: | They propose a Chinese CKG generated from multilingual PLMs that is translated into Chinese . they propose 'generate-by-category' strategy to reduce invalid generation . |
| Outcome: | The proposed CKG has high quality and diversity, surpassing the direct translation version of similar English CKGs. |
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)
Copied to clipboard
| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
| Approach: | They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. |
| Outcome: | The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets. |
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC) |
| Approach: | They propose three types of negatives to improve contrastive learning to improve learning efficiency. |
| Outcome: | The proposed model outperforms embedding-based methods on several benchmark datasets. |
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance. |
| Approach: | They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task. |
| Outcome: | The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction. |