Abstract
Multilingual search optimization is important to help users obtain precise and relevant information in different languages. However, traditional search techniques typically have a hard time with semantic subtleties, cultural differences, and the difficulties of low-resource languages. In this work, we propose a new approach combining BERT's power (Bidirectional Encoder Representations from Transformers) with domain-specific product knowledge bases to improve multilingual search performance. With natural language understanding capabilities of the advanced BERT model and structured product knowledge integrated, our model enhances query understanding, reduces context irrelevancy, and improves the accuracy of search results. The proposed system is tested on high- and low-resource multilingual datasets, and it achieved considerable improvements in precision, recall, and Mean Reciprocal Rank (MRR) over baseline methods, including TF-IDF and BM25. Additionally, the model could better resolve ambiguous queries and provide domain-specific insights by adding product knowledge bases. The impact of this approach on user experience, cultural adaptation, and cross-linguistic consistency is also evaluated in the study. Thorough model comparison and ablation analysis were demonstrated to show that the combination of BERT and knowledge bases outperforms traditional and modern search optimization techniques. This research makes the case for AI-powered multilingual search optimization and offers a basis for continued innovation in global search systems.
Keywords
Multilingual Search, BERT, AI, Product Knowledge Bases, Search Optimization, Natural Language Processing, Cross-lingual Search
Citations
IRE Journals:
Martin Louis
"Multilingual Search Optimization Using BERT AI and Product Knowledge Bases" Iconic Research And Engineering Journals Volume 2 Issue 9 2019 Page 208-223
IEEE:
Martin Louis
"Multilingual Search Optimization Using BERT AI and Product Knowledge Bases" Iconic Research And Engineering Journals, 2(9)
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