Papers with Link Prediction
Jack the Reader – A Machine Reading Framework (P18-4)
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Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
| Challenge: | Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. |
| Approach: | They propose a framework for Machine Reading that allows for quick prototyping by component reuse and evaluation of new models on existing datasets. |
| Outcome: | The proposed framework supports question answering, natural language inference and link prediction tasks. |
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 Temporal Knowledge Graph Embeddings with Relational and Time Curvatures (2021.findings-acl)
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| Challenge: | Existing knowledge Graph models for Link Prediction are insensitive to time. |
| Approach: | They propose a time-aware extension of ATTH model which defines curvature of a Riemannian manifold as the product of both relation and time. |
| Outcome: | The proposed model can achieve competitive or even better performance than the state-of-the-art model on Temporal KGs, albeit its nontemporality. |
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. |
Inductive Link Prediction in N-ary Knowledge Graphs (2025.coling-main)
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| Challenge: | Existing methods to predict missing elements in NKGs are fixed and therefore cannot be used in real-world situations. |
| Approach: | They propose a task to predict missing elements in unseen facts involving unseent entities and roles in emerging NKGs by embedding unseense entities and role-encoding neural networks. |
| Outcome: | The proposed task outperforms representative models across all datasets. |
Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)
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| Challenge: | Existing methods to predict missing elements in hyper-relational facts require high-quality data. |
| Approach: | They propose a task to predict a missing entity in a hyper-relational fact with limited support instances. |
| Outcome: | The proposed model outperforms existing models on three datasets. |
How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs? (2025.emnlp-main)
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| Challenge: | Existing methods to predict missing triples in Knowledge Graphs are limited by semantic information. |
| Approach: | They propose a method to leverage sememe knowledge to enhance LP . LP is a technique that integrates structural and textual information into a Knowledge Graph . |
| Outcome: | The proposed method improves LP performance in English and Chinese . it improves on WN18RR, HN7 and CWN5, respectively . |