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- Title
Advancements in Embedded Static Knowledge Graph Completion Research.
- Authors
WU Yujie; XI Xuefeng; CUI Zhiming
- Abstract
Knowledge graphs are widely used and semantically rich data representations, which is increasingly becoming a crucial technology in the field of knowledge engineering. However, real-world knowledge graphs often suffer from incompleteness and ambiguity, hindering their application performance. Knowledge graph completion techniques aim to enrich the content of knowledge graphs by predicting missing entities or relations, has been a hot research topic in recent years. In particular, embedding-based approaches have made remarkable progress in knowledge graph completion tasks. It reviews recent embedding- based static knowledge graph completion methods, categorizing them based on approaches such as translation- based models, tensor factorization, neural network models, and pre- trained language models. These methods achieve an improved semantic representation and inferential capabilities by embedding entity relations into continuous vector spaces. At the same time, it has potential advantages in capturing complex relationships between entities and utilizing graph structural information.
- Subjects
KNOWLEDGE graphs; ARTIFICIAL neural networks; LANGUAGE models; VECTOR spaces; FACTORIZATION; GRAPH algorithms
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 12, p34
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2310-0221