Papers with KG completion
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
Copied to clipboard
Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network (2021.acl-long)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) are incomplete because of the large number of benchmark datasets that are not representative of real KGs. |
| Approach: | They develop a deep convolutional network that utilizes textual entity representations to distill the knowledge from the convolution into a student network that re-ranks promising candidate entities. |
| Outcome: | The proposed model outperforms recent methods in a realistic setting where dense connectivity is not guaranteed. |
Multi-Channel Graph Neural Network for Entity Alignment (P19-1)
Copied to clipboard
| Challenge: | Existing methods to learn alignment-oriented knowledge graph embeddings suffer from structural heterogeneity and limited seed alignments. |
| Approach: | They propose a multi-channel Graph Neural Network model to learn alignment-oriented knowledge graph embeddings by encoding two KGs via multiple channels. |
| Outcome: | The proposed model is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. |
Counterfactual Reasoning with Knowledge Graph Embeddings (2024.eacl-long)
Copied to clipboard
| 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. |
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained language models (LMs) are a powerful transfer learning approach for knowledge graph (KG) completion. |
| Approach: | They propose a parameter-lite transfer learning approach for pretrained language models for knowledge graph (KG) completion. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a knowledge graph completion benchmark by tuning 1% of the parameters. |
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for learning missing facts in knowledge graphs are limited by insufficiency of alignment information and inconsistency of described facts. |
| Approach: | They propose a framework for embedding learning and ensemble knowledge transfer across KGs. |
| Outcome: | The proposed framework improves state-of-the-art methods on language-specific KGs. |
Joint Multilingual Knowledge Graph Completion and Alignment (2022.findings-emnlp)
Copied to clipboard
Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, Mathias Niepert
| Challenge: | Existing work on multilingual KG completion has focused on entity and relation alignments, but understanding of how it can aid multilingual alignments is limited. |
| Approach: | They propose to combine two components that jointly accomplish KG completion and alignment. |
| Outcome: | The proposed model outperforms existing competitive baselines on a public multilingual benchmark and achieves state-of-the-art results. |
A2N: Attending to Neighbors for Knowledge Graph Inference (P19-1)
Copied to clipboard
| Challenge: | Existing knowledge graph completion methods learn a fixed embedding for every entity, which is suboptimal as it requires memorizing and generalizing to all possible entity relationships. |
| Approach: | They propose a method which learns query-dependent representations of entities by combining relevant neighborhood of an entity. |
| Outcome: | The proposed model performs competitively or better than existing state-of-the-art models for knowledge graph completion. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
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. |