Papers with link prediction tasks
Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features (2025.coling-industry)
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| Challenge: | Quantum computing is rapidly evolving in both physics and computer science due to its potential to solve complex quantum physics problems and accelerate computational processes. |
| Approach: | They propose to initialize node features using LLMs to enhance node representations for link prediction tasks in graph neural networks. |
| Outcome: | The proposed method compared to traditional node embedding techniques on a quantum computing semantic network and demonstrated efficacy compared with other methods. |
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering (2022.acl-srw)
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| Challenge: | Existing methods to embed learning use a standard Neural Networks (NN) backward mechanism, duplicating its memory consumption. |
| Approach: | They propose a memory-efficient KG embedding model that embeds knowledge graphs as 3rd-order binary tensors. |
| Outcome: | The proposed model yields comparable performance on link prediction and KG-based question answering tasks. |
Conformalized Answer Set Prediction for Knowledge Graph Embedding (2025.naacl-long)
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| Challenge: | Knowledge graph embeddings (KGE) map entities and predicates into numerical vectors, providing non-classical reasoning capabilities based on similarities and analogies between entities and relations. |
| Approach: | They propose to use knowledge graph embeddings to provide non-classical reasoning capabilities by exploiting similarities and analogies between entities and relations. |
| Outcome: | The proposed model can generate answer sets with probabilistic guarantees on four benchmark datasets and is scaled well with respect to the difficulty of the query. |
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. |
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding (2020.findings-emnlp)
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
| Approach: | They propose a KGE framework with an automatic type embedding mechanism which can be easily integrated into any existing KGE model. |
| Outcome: | The proposed model can model and infer all the relation patterns and complex relations compared to state-of-the-art models on four datasets. |
Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)
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| Challenge: | Existing knowledge graphs are incomplete and therefore lack interpretability. |
| Approach: | They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs. |
| Outcome: | The proposed model outperforms baselines on link prediction tasks on four real-world datasets. |
HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)
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| Challenge: | Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information. |
| Approach: | They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner. |
| Outcome: | The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines. |
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)
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| Challenge: | Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies. |
| Approach: | They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. |
| Outcome: | The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance. |
LPNL: Scalable Link Prediction with Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on graph learning with large language models have focused on the link prediction task on large graphs. |
| Approach: | They propose a framework for scalable link prediction on large-scale heterogeneous graphs based on large language models. |
| Outcome: | The proposed framework outperforms baselines in link prediction tasks on large graphs. |
Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods are built on Euclidean space, which are difficult to handle hierarchical structures. |
| Approach: | They propose a KGE model with extended Poincaré Ball and polar coordinate system to capture hierarchical structures. |
| Outcome: | The proposed model captures hierarchical relationships with extended Poincaré Ball and polar coordinate system in hyperbolic space and achieves state-of-the-art results on part of link prediction tasks. |
Scientific Paper Extractive Summarization Enhanced by Citation Graphs (2022.emnlp-main)
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| Challenge: | citation graphs can be used to extract scientific papers under different conditions. |
| Approach: | They propose a multi-granularity unsupervised summarization model that fine tunes a pre-trained encoder model on the citation graph by link prediction tasks. |
| Outcome: | The proposed model outperforms baseline models on a public benchmark dataset. |
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion (2022.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs can hardly model temporal relation patterns, lacking of interpretability. |
| Approach: | They propose a temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space and relations as complex vectors in Hamilton’s quaterniont space. |
| Outcome: | The proposed method can model key patterns of relations in TKG, such as symmetry, asymmetry, and inverse, and can capture time-evolved relations by theory. |
OpticE: A Coherence Theory-Based Model for Link Prediction (2022.coling-1)
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| Challenge: | Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). |
| Approach: | They propose a new embedding approach based on the physical phenomenon of optical interference to reduce the semantic ambiguity in KGs. |
| Outcome: | The proposed model can compete with existing methods on KG benchmarks. |
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level (2023.acl-long)
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Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin
| Challenge: | Existing research on HKGs rarely models the graphical and sequential structure of HKG, limiting their representation. |
| Approach: | They propose a Hierarchical Attention model for HKG Embedding that includes global-level and local-level attention to model the graphical structure of HKGs. |
| Outcome: | The proposed model achieves state-of-the-art performance on HKG standard datasets and addresses the issue of HKG multi-position prediction for the first time. |
MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding (2024.emnlp-main)
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| Challenge: | Existing knowledge graph embedding models suffer from Z-paradox, a deficiency in expressiveness . Embedding-based models map each entity and relation into a vector or matrix . |
| Approach: | They propose a new knowledge graph embedding model that does not suffer from Z-paradox while preserves strong expressiveness to model various relation patterns with theoretical justification. |
| Outcome: | The proposed model outperforms existing models on link prediction tasks while maintaining strong expressiveness. |
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction (2022.lrec-1)
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Mehdi Mirzapour, Waleed Ragheb, Mohammad Javad Saeedizade, Kevin Cousot, Helene Jacquenet, Lawrence Carbon, Mathieu Lafourcade
| Challenge: | Knowledge graphs are used for information extraction, search engines, question answering, and recommendation systems. |
| Approach: | They propose a French knowledge graph dataset based on RezoJDM. |
| Outcome: | The proposed dataset can be used in many downstream tasks for the French language . it shows that it embeds knowledge graph baselines for link prediction tasks . |
Should We Use a Fixed Embedding Size? Customized Dimension Sizes for Knowledge Graph Embedding (2025.coling-main)
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| Challenge: | Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, which is crucial for knowledge completion, fusion, and inference. |
| Approach: | They propose to embed entities and relations into a low-dimensional space to enable knowledge Graphs to be effectively used by downstream AI tasks. |
| Outcome: | The proposed framework is universal and flexible, suitable for various KGE models. |
Knowledge Graph Representation Learning using Ordinary Differential Equations (2021.emnlp-main)
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| Challenge: | Knowledge Graph Embeddings (KGEs) map entities and relations from knowledge graphs into a geometric space. |
| Approach: | They propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs) they represent each relation (edge) in a knowledge graph as a vector field on several manifolds. |
| Outcome: | The proposed model can preserve graph characteristics including structural aspects and semantics and avoid wrong inferences. |
Less Is MuRE: Revisiting Shallow Knowledge Graph Embeddings (2025.emnlp-main)
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| Challenge: | Knowledge graphs encode knowledge in the form of subject-predicate-object triples, which is notoriously incomplete. |
| Approach: | They propose a framework for analyzing existing shallow knowledge graph models and their extensions. |
| Outcome: | The proposed framework shows that MuRE and ExpressivE are highly competitive . it can capture the same class of rule bases as state-of-the-art region-based embedding models. |