A Semantic Search Engine for Turkish and English Research Resources

Abstract

Research resources are growing in volume at an exponential rate across disciplines and languages. This exponential increase has created a pressing need for intelligent search systems that can help researchers efficiently access relevant academic material. To overcome this issue, this study introduces a bilingual semantic search engine designed to retrieve academic articles written in both Turkish and English. The primary goal is to improve the accuracy and relevance of academic information retrieval by using modern Natural Language Processing techniques. Instead of relying on traditional keyword-based search methods, the system leverages transformer-based sentence embedding models. To capture semantic meaning more effectively, MiniLM-L6v2, paraphrase-multilingual-MiniLM-L12-v2 and multilingual-e5-base models were chosen for their multilingual capabilities and sentence-level embedding performance. To assess the quality of search results, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG) were used. These metrics were calculated for each model across both language groups. Evaluation results show that the multilingual-e5-base model consistently outperformed the other models in both MAP and nDCG scores, demonstrating superior semantic understanding and multilingual alignment. The system also features a simple and responsive Streamlit-based interface that allows for real-time querying and result display. © 2025 IEEE.