Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for inductive knowledge graph completion are underperforming . implausible entities are not ranked and only the most informative path is taken into account . |
| Approach: | They propose to use a rule-based approach to find plausible triples missing from a given KG. |
| Outcome: | The proposed models outperform state-of-the-art methods on inductive knowledge graph completion. |
Similar Papers
A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)
Copied to clipboard
| Challenge: | Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. |
| Approach: | They propose a protocol to evaluate KGC methods that is robust to handle bias in the model, which can substantially affect the final results. |
| Outcome: | The proposed evaluation protocol is robust to handle bias in the model, which can substantially affect the final results. |
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Knowledge Graph Completion (KGC) are unsatisfactory . |
| Approach: | They propose to use rule-guided train/test generation instead of conventional random split to ensure that each testing sample is predictable with supportive data in the training set. |
| Outcome: | The proposed model improves on existing benchmarks in inferential ability, assumptions, and patterns. |
Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules (2022.emnlp-main)
Copied to clipboard
Prithviraj Sen, Breno William Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Salim Roukos, Alexander Gray
| Challenge: | Knowledge base completion (KBC) is a human-interpretable dialect . rule-based KBC has a high quality but low accuracy . |
| Approach: | They propose to use logical neural networks to learn both kinds of rules in a common framework using gradient-based optimization. |
| Outcome: | The proposed method improves by 10% relative to SotA rule-based methods and by combining it with knowledge graph embeddings it achieves an additional 7.5% relative improvement. |
Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion (2023.acl-short)
Copied to clipboard
| Challenge: | Recent studies show that high-quality rule sets struggle with high coverage. |
| Approach: | They propose three simple augmentations to existing rule sets to improve results . they propose transforming rules to their abductive forms and generating equivalent rules that use inverse forms of constituent relations . |
| Outcome: | The proposed methods achieve up to 7.1 pt MRR and 8.5 pT Hits@1 gains over using rules without augmentations. |
StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
Copied to clipboard
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| Challenge: | Knowledge graphs (KGs) represent incomplete knowledge bases. |
| Approach: | They propose to use language models to extract semantic information from text descriptions while using Message Passing Neural Networks to capture structural information. |
| Outcome: | The proposed model achieves state of the art on three challenging inductive baselines. |
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. |
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. |
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs. |
| Approach: | They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information. |
| Outcome: | The proposed method outperforms a general GNN-based approach for KGC. |
Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing knowledge graphs lack robustness and incompleteness to provide link prediction. |
| Approach: | They propose to capture prior schema-level interactions related to relations by leveraging entity type information and introduce schema-guided negatives to bolster the efficiency of normal contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple established metrics across multiple datasets for link prediction. |
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
Copied to clipboard
| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |