Pattern Over-Generalization of Knowledge Graph Embedding
Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relations into a low-dimensional vector space. It is crucial for KGE models to effectively capture inference patterns (patterns) inherent in KGs, such as symmetry/antisymmetry, inversion and composition. Although recent KGE models exhibit strong capabilities in modeling such diverse patterns, they suffer from inherent limitations stemming from pattern over-generalization, where embeddings learned from only a single pattern instance inevitably generalize that pattern to all related instances, i.e., generalize the pattern universally. To address th...
| Authors | Junsik Kim, Kangil Kim |
|---|---|
| Primary category | cs.CL |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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