| Challenge: | Existing knowledge graph embedding approaches model entities and relations in KGs using real-valued, complex-value, or hypercomplex-value representations. |
| Approach: | They propose a geometric algebra-based KG embedding framework which uses multivector representations and the geometric product to model entities and relations. |
| Outcome: | The proposed framework outperforms state-of-the-art models for link prediction. |
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Knowledge GeoGebra: Leveraging Geometry of Relation Embeddings in Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Knowledge graph embedding models are limited to the algebra and geometry of the entity embeddable space, the algebra of the relation embeddible space, and the interaction between relation and entity embeds. |
| Approach: | They propose a method that leverages the geometry of relation embeddings and generalizes it with the concept of a butterfly curve, consecutively. |
| Outcome: | The proposed model outperforms existing models on the WN18RR, FB15K-237 and YouTube benchmarks. |
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. |
| Approach: | They propose to use KG embedding methods to represent entities and relations as vectors in a high-dimensional space. |
| Outcome: | The proposed methods represent entities and relations in KGs as vectors in a high-dimensional space. |
SpeedE: Euclidean Geometric Knowledge Graph Embedding Strikes Back (2024.findings-naacl)
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| Challenge: | Geometric knowledge graph embedding models (gKGEs) have shown great potential for knowledge graph completion (KGC) however, contemporary gKges require high embeddable dimensionalities or complex embeddances for good KGC performance, drastically limiting their time and space efficiency. |
| Approach: | They propose a lightweight Euclidean gKGE that provides strong inference capabilities and significantly outperforms state-of-the-art gGKGEs. |
| Outcome: | The proposed model outperforms state-of-the-art gKGEs on YAGO3-10 and WN18RR while significantly increasing their efficiency. |
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. |
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. |
| Approach: | They propose a method that directly learns embeddings of entity-pairs for KG alignment. |
| Outcome: | The proposed approach can achieve state-of-the-art on five real-world datasets. |
EventKE: Event-Enhanced Knowledge Graph Embedding (2021.findings-emnlp)
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| Challenge: | Experimental results show that events can greatly improve the quality of KG embeddings on multiple downstream tasks. |
| Approach: | They propose an event-enhanced KG embedding model that incorporates events into KGs . they first incorporate event nodes by building a heterogeneous network with event argument links . |
| Outcome: | The proposed model incorporates event nodes into the original knowledge graphs . it can be used to fuse event information into the KG embeddings on multiple tasks . |
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding (2024.findings-emnlp)
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| Challenge: | Existing knowledge graph embeddings (KGs) are limited in their flexibility and difficulties in generalizing them for higher-dimensional rotations. |
| Approach: | They propose a KGE model employing matrices for entities and block-diagonal orthogonal matrics with Riemannian optimization for relations that captures several relation patterns that rotation-based methods can identify. |
| Outcome: | The proposed model outperforms state-of-the-art models while reducing the number of relation parameters. |
Dual Complex Number Knowledge Graph Embeddings (2024.lrec-main)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . extending to such sophisticated spaces increases the amount of parameters, which greatly reduces the parameter efficiency. |
| Approach: | They propose a new knowledge graph embedding method that maps entities to the dual complex number space and represents relations as rotations in 2D space via dual complex multiplication. |
| Outcome: | Experiments on multiple benchmark knowledge graphs show that the proposed method improves link prediction and path query answering. |
Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020.acl-main)
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| Challenge: | Existing methods for predicting missing facts do not account for hierarchical and logical patterns in KGs. |
| Approach: | They propose a class of hyperbolic KG embedding models that capture hierarchical and logical patterns. |
| Outcome: | Experimental results show that the proposed method improves by 6.1% in mean reciprocal rank in low dimensions over previous methods. |
Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
| Approach: | They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs . |
| Outcome: | The proposed approach can extend existing KGE models, and validates against baselines. |