| Challenge: | Existing methods to predict missing links in knowledge graphs are lacking. |
| Approach: | They propose a method to automatically rank paths between a source and target entity pair using a knowledge base completion model. |
| Outcome: | The proposed method can rank and rank paths in biomedical knowledge graphs with a KBC model. |
Similar Papers
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Knowledge Base Completion Meets Transfer Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge bases are tedious and require a large amount of labor to build. |
| Approach: | They propose a method that allows for transfer of knowledge from one collection of facts to another without entity or relation matching. |
| Outcome: | The proposed method is the most impactful on small datasets, showing a 6% increase in rank and 65% decrease in rank over the previous best method. |
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)
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| Challenge: | Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. |
| Approach: | They propose a context-enriched framework for KGC that uses a large language model to generate potential answers for each query triple. |
| Outcome: | The proposed framework improves on FB15k237 and WN18RR datasets. |
Graph Pattern Entity Ranking Model for Knowledge Graph Completion (N19-1)
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| Challenge: | Knowledge graph embedding models are so called-black box and are hard to interpret. |
| Approach: | They propose to use graph patterns to construct an entity ranking system for each graph pattern and evaluate them using a ranking system. |
| Outcome: | The proposed model outperforms other state-of-the-art models on standard metrics such as HITS@n and MRR. |
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (N18-2)
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| Challenge: | Existing knowledge base embedding models are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose a convolutional neural network embedding model for knowledge base completion that captures global relationships and transitional characteristics. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion (2020.findings-emnlp)
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| Challenge: | Existing methods for learning non-parametric representations of entities and relations are based on tensor factorization or sophisticated neural approaches. |
| Approach: | They propose a case-based reasoning system that retrieves ‘cases’ that are similar to the given problem and then stores them in its parameters. |
| Outcome: | The proposed model outperforms state-of-the-art methods on several benchmark datasets and is non-parametric and grows dynamically as new entities and relations arrive in the KB. |
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (N19-1)
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| Challenge: | a novel method for mapping unrestricted text to knowledge graph entities is proposed . a proof-of-concept experiment has encouraging results comparable to those of state-of the-art systems. |
| Approach: | They propose a method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. |
| Outcome: | The proposed method produces highly interpretable predictions comparable to state-of-the-art systems. |
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision (P18-2)
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| Challenge: | State-of-the-art knowledge base completion models make frequent errors when ranking entities that are not compatible with the type required by the relation. |
| Approach: | They propose to enhance each base factorization with two type-compatibility terms between entity-relation pairs and combine the signals in a novel manner. |
| Outcome: | The proposed model achieves 7% MRR gains over baseline models and predicts supervised types better than baseline models. |
A2N: Attending to Neighbors for Knowledge Graph Inference (P19-1)
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| 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. |
Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules (2022.emnlp-main)
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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. |