| 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. |
| Outcome: | Experiments on two knowledge graph datasets show the proposed model can handle complex relations and encode relation patterns. |
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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). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
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Knowledge Graph Alignment with Entity-Pair Embedding (2020.emnlp-main)
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| Challenge: | Existing methods for Knowledge Graph (KG) alignment are not satisfactory. |
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SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)
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| Challenge: | Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory. |
| Approach: | They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. |
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A Mutual Information Perspective on Knowledge Graph Embedding (2025.acl-long)
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| Challenge: | Existing knowledge graph embedding techniques suffer from high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations. |
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RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (2020.coling-main)
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| Challenge: | Existing approaches for knowledge graph embedding have limitations in complex vector space . embeddability of one-to-many relations is not explicitly alleviated . |
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Towards Understanding the Geometry of Knowledge Graph Embeddings (P18-1)
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| Challenge: | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embeddable methods. |
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A Semantic Filter Based on Relations for Knowledge Graph Completion (2021.emnlp-main)
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| Challenge: | Knowledge graph embedding is a new form of knowledge graphing that allows for better link prediction. |
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
| Approach: | They extend the random walk model of word embeddings to Knowledge Graph Embeddings (KGEs) they propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
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TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction (2022.emnlp-main)
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| Challenge: | Existing knowledge graph embedding methods restrict entities on hyper-ellipsoid surfaces, resulting in suboptimal knowledge graph completion. |
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