| 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. |
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Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities. |
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| Challenge: | Existing subgraph-based models focus on predicting missing relations in knowledge graphs . a new meta-learning model extracts concepts from entities to construct commonsense subgraphs based on semantic information . |
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Link Prediction on N-ary Relational Facts: A Graph-based Approach (2021.findings-acl)
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| Challenge: | Existing work on knowledge graphs (KGs) focused on binary relations, but higher-arity relations are ubiquitous in real-world KGs. |
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A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Semi-inductive link prediction (LP) is a task of predicting facts for new, previously unseen entities based on context information. |
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| Challenge: | Existing methods for predicting knowledge graphs rely on the rich structure of the knowledge graph. |
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Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)
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| Challenge: | Knowledge Graph (KG) inductive reasoning is widely adopted in various applications. |
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Type-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models (2025.emnlp-main)
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Alessandro De Bellis, Salvatore Bufi, Giovanni Servedio, Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio
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