Challenge: Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective.
Approach: They propose a system which tunes the parameters of Conditional Soft Prompts generated by entities and relations representations to maintain a balance between textual and structural knowledge.
Outcome: The proposed components outperform baseline models on three static and temporal benchmarks.

Similar Papers

Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach (2022.findings-acl)

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Challenge: Pre-trained language models capture factual knowledge from massive texts . but they are still quite behind the SOTA KGC models in terms of performance .
Approach: They propose to use open-world assumption to evaluate PLM-based knowledge graph completion models . they propose to convert each triple and its support information into natural prompt sentences .
Outcome: The proposed model is more accurate under the open-world assumption (OWA) this setting manual checks the correctness of knowledge that is not in KGs.
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)

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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.
Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models (2023.findings-acl)

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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.
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)

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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.
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)

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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.
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)

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Challenge: Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes.
Approach: They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning.
Outcome: The proposed framework outperforms state-of-the-art methods on two KGC tasks across four benchmark datasets.
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding (2022.emnlp-main)

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Challenge: Existing knowledge-enhanced pre-trained language models (PLMs) introduce redundant factual knowledge from knowledge bases and require complex modules.
Approach: They propose a knowledge prompting-based PLM framework that incorporates factual knowledge into PLMs.
Outcome: The proposed framework can be flexibly combined with existing mainstream PLMs.
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)

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Challenge: Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities . traditional embedding-based methods infer missing links using only training data . a pre-trained language model (PLM)-based KGC may be ineffective in practical applications .
Approach: They propose to use knowledge Graph Completion (KGC) to infer unseen relationships . traditional embedding-based KGC methods infer missing links only from training data . they argue that pre-trained language models acquire inference abilities through pre-training .
Outcome: The proposed method improves performance even though it does not use memorized knowledge.
Path-enhanced Pre-trained Language Model for Knowledge Graph Completion (2025.findings-emnlp)

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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.
Bilateral Masking with prompt for Knowledge Graph Completion (2024.findings-naacl)

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Challenge: Existing word matching methods fail to obtain satisfactory single embedding representations for entities.
Approach: They propose a bi-encoder-based approach to enhance entity representations by using prompts to narrow the distance between the predicted entity and the known entity.
Outcome: The proposed model achieves state-of-the-art performance on the WN18RR dataset.

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