Khmer Semantic Search Engine: Digital Information Access and Document Retrieval

The search engine process is crucial for document content retrieval. For Khmer documents, a tool is needed to extract essential keywords. Despite the daily generation of significant Khmer content, Cambodians struggle to find necessary documents due to the lack of an effective semantic searching tool. Even Google does not deliver high accuracy for Khmer content. Semantic search engines improve search results by employing advanced algorithms to understand various content types. With the rise in Khmer digital content such as reports, articles, and social media feedback enhanced search capabilities are essential. This research proposes the first Khmer Semantic Search Engine (KSE), designed to improve traditional Khmer search methods. Utilizing semantic matching techniques and formally annotated semantic content, our tool extracts meaningful keywords from user queries performs precise matching, and provides the best matching offline documents and online URL documents. We propose two semantic search frameworks based on keyword extraction and semantic search matching. Additionally, we developed tools for data preparation, including document addition and manual keyword extraction. To evaluate performance, we created a ground truth dataset and discussed issues related to searching and semantic search. Our findings show how understanding search term semantics can lead to more accurate results.