Papers with Knowledge Graph Completion
Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)
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Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
| Challenge: | Knowledge graphs (KGs) are incomplete and miss some information. |
| Approach: | They propose to learn entity representations via a graph structure that uses Seen-entities, Unseen-Entities and words as nodes created from the descriptions of all entities. |
| Outcome: | The proposed method improves relation prediction for the entity pairs containing Unseen-entities. |
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. |
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graphs are unable to handle non-English textual information. |
| Approach: | They propose a task of automatic Knowledge Graph Completion to bridge the gap between English and non-English textual information. |
| Outcome: | The proposed method bridges the gap between the quantity and quality of textual information between English and non-English languages. |
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. |
Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information (2023.findings-emnlp)
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| Challenge: | Knowledge graph completion (KGC) methods are computationally intensive and impractical for large-scale KGs. |
| Approach: | They propose to include node neighborhoods as additional information to improve KGC methods based on language models. |
| Outcome: | The proposed method outperforms KGT5 and conventional methods on inductive and transductive Wikidata subsets and shows its importance. |
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion (2022.coling-1)
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| Challenge: | Knowledge Graph Completion (KGC) has been extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. |
| Approach: | They propose a generative framework that could tackle different verbalizable graph structures by unifying the representation of KG facts into "flat" text. |
| Outcome: | The proposed framework outperforms many competitive baselines and sets new state-of-the-art performance on five benchmarks. |
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding (2022.coling-1)
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| Challenge: | Existing TKGC methods are based on deterministic vector embeddings, which are not flexible and expressive enough. |
| Approach: | They propose a method that maps entities and relations to multivariate Gaussian processes by mapping global trends and local fluctuations in TKGs. |
| Outcome: | The proposed method can predict global trends and local fluctuations in the TKGs and can be optimized on two real-world benchmark datasets. |
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. |
A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)
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| 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. |
Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage? (2021.emnlp-main)
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| Challenge: | Knowledge Graph Completion (KGC) attempts to learn missing links from subsets. |
| Approach: | This survey/position paper discusses ways to improve coverage of resources such as WordNet. |
| Outcome: | The proposed method improves WordNet coverage by reducing the number of words in the sample and reducing unbalanced corpora. |
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion (2021.acl-long)
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| 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. |
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. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
Generating and Evaluating Plausible Explanations for Knowledge Graph Completion (2024.acl-long)
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| Challenge: | Existing XAI approaches focus on learning algorithmic explanations, but are not plausible for users. |
| Approach: | They propose a path-based explanation method that meets human-centric explainability constraints and enhances plausibility. |
| Outcome: | The proposed method meets human-centric explainability constraints and enhances plausibility. |
Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting (2023.findings-acl)
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| 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. |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete. |
| Approach: | They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook . |
| Outcome: | The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain. |
Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning (2024.emnlp-main)
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| Challenge: | Existing contrastive methods focus on individual triples, overlooking the broader structural connectivities and topologies of KGs. |
| Approach: | They propose a new contrastive learning framework that incorporates four tasks specifically tailored to KG data: Vertex-level CL, Neighbor-level Cl, Path-levelCL, and Relation composition level CL. |
| Outcome: | The proposed framework achieves SOTA performance under standard supervised and low-resource settings. |
Joint Completion and Alignment of Multilingual Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for knowledge graph completion are incomplete, as curators struggle to keep up with the real world. |
| Approach: | They propose a multitask approach to solve missing facts in incomplete Knowledge Graphs . they add a relation representation to the existing KG embedding scheme . |
| Outcome: | The proposed system outperforms existing models in seven languages compared to existing models . it also outperformed existing models, underscoring the value of joint alignment and completion. |
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion (2024.emnlp-main)
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| Challenge: | Existing approaches to knowledge graph completion have not integrated the structural attributes of knowledge graphs with the textual descriptions of entities to generate robust entity encodings. |
| Approach: | They propose to integrate structural information from knowledge graphs with textual descriptions of entities to generate robust entity encodings. |
| Outcome: | The proposed model improves on the standard evaluation metric, Mean Reciprocal Rank (MRR), while surpassing the current best model on the Wikidata5M dataset. |
CompleQA: Benchmarking the Impacts of Knowledge Graph Completion Methods on Question Answering (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on Knowledge Graph Completion as an end in itself, neglecting its potential impact on subsequent applications. |
| Approach: | They propose a benchmark to assess the impact of representative KGC methods on Knowledge Graph Question Answering (KGQA) they use a knowledge graph with 3 million triplets across 5 distinct domains to evaluate their results. |
| Outcome: | The proposed benchmark compares four well-known methods with two state-of-the-art systems to assess the impact of incomplete knowledge graphs on KGQA. |
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)
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| 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. |
Prior Relational Schema Assists Effective Contrastive Learning for Inductive Knowledge Graph Completion (2024.lrec-main)
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| 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. |