| Challenge: | Knowledge graphs contain only a small subset of all possible facts . link prediction is a task of inferring missing facts based on existing facts - knowledge graphs are expensive and lack of information is needed to add new information. |
| Approach: | They propose a linear model based on Tucker decomposition of knowledge graph triples . they show that the model is expressive and has sufficient bounds on its embedding dimensionalities . |
| Outcome: | The proposed model outperforms state-of-the-art models across standard datasets and acts as a strong baseline for more elaborate models. |
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| Challenge: | Knowledge graph embedding is a new form of knowledge graphing that allows for better link prediction. |
| Approach: | They propose to use relational embedding to fit symmetry/antisymmetry and combination relationships. |
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Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings (2021.naacl-main)
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| Challenge: | Existing knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama) is valid only from 2009 to 2017. |
| Approach: | They propose a time-aware knowledge graph embebdding approach which performs 4th-order tensor factorization of a Temporal knowledge graph using a Linear temporal regularizer and Multivector embeddings. |
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A Survey of Link Prediction in N-ary Knowledge Graphs (2025.emnlp-main)
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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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LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
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Joint Language Semantic and Structure Embedding for Knowledge Graph Completion (2022.coling-1)
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| Challenge: | Existing methods to complete knowledge triplets rely on structures or semantics, but use semantics to improve performance. |
| Approach: | They propose to embed semantics in the natural language description of knowledge triplets with their structure information. |
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PairRE: Knowledge Graph Embeddings via Paired Relation Vectors (2021.acl-long)
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| Challenge: | Existing knowledge graph embedding methods fail to solve two major problems at the same time, leading to unsatisfactory results. |
| Approach: | They propose a model with paired vectors for each relation representation that can be adaptively adjusted to fit for different complex relations. |
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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. |
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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. |
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Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
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A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (N18-2)
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| Challenge: | Existing knowledge base embedding models are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose a convolutional neural network embedding model for knowledge base completion that captures global relationships and transitional characteristics. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |