Knowledge graph completion based on iteratively learning embeddings and noise-aware rules
Abstract Knowledge graph completion (KGC) is used to infer new facts from existing facts. Embedding-based KGC methods efficiently predict new facts by computing similarities among embeddings, whereas rule-based KGC methods achieve accuracy by applying logical rules. Both methods are combined in stud...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00148-6 |
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