Document searching using queries that can understand the context can affect the intent and purpose of the user's desire when searching documents. Many studies have been conducted on understanding the context of the query, but differences in terms of language can lead to different methods of context understanding; therefore, methods implemented in the previous studies need to be improved. In this paper, we proposed a query expansion method based on BabelNet search and Word Embedding (BabelNet Embedding). Query expansion method focuses on developing queries based on semantic relationships on queries to understand the context of the query. Candidate queries were developed by finding synonyms, measuring similarity using WordNet, Word Embedding on all articles of Wikipedia, and BableNet Embedding on articles Wikipedia Online. We compared our proposed method with the existing semantic query expansion. Our result provided better result in retrieving relevant document with accuracy of 89% in searching Arabic documents.
|Number of pages||12|
|Journal||International Journal of Intelligent Engineering and Systems|
|Publication status||Published - 2019|
- Arabic document
- BabelNet embedding
- Semantic query expansion
- Word embedding