An Intelligent Recommendation System for English Translation Based on Knowledge Graph in Cross-Cultural Communication
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Abstract
Accurate cross-cultural translation is essential for international scientific collaboration and technical knowledge dissemination, including communication in electromagnetic waves, antennas, and propagation research. To address the limitations of conventional machine translation in contextual understanding and cultural adaptation, this study proposes an intelligent English translation recommendation system based on a multilingual knowledge graph. The framework integrates knowledge graph construction, cross-lingual entity alignment, context-aware translation enhancement, and culturally adaptive recommendation to improve semantic consistency and translation reliability. By combining structured knowledge representation with graph-based semantic reasoning, the proposed approach effectively resolves ambiguity in domain-specific terminology and enhances personalized translation recommendations across diverse cultural contexts. Experimental results demonstrate that the system achieves a BLEU score of 68.53 and significantly outperforms conventional translation methods in both translation quality and user satisfaction. The proposed framework provides a reliable solution for multilingual technical communication and offers valuable support for the accurate dissemination of scientific information and collaborative research in globally distributed engineering disciplines, including electromagnetic and wireless communication applications.
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