Semantic Arabic Information Retrieval Framework
This addresses the need for more accurate and context-aware retrieval in Arabic, which is incremental as it builds on existing IR methods by adding semantic components.
The authors tackled the problem of polysemy and synonymy in Arabic information retrieval by proposing a semantic framework, resulting in improved accuracy and speed for handling semantic queries.
The continuous increasing in the amount of the published and stored information requires a special Information Retrieval (IR) frameworks to search and get information accurately and speedily. Currently, keywords-based techniques are commonly used in information retrieval. However, a major drawback of the keywords approach is its inability of handling the polysemy and synonymy phenomenon of the natural language. For instance, the meanings of words and understanding of concepts differ in different communities. Same word use for different concepts (polysemy) or use different words for the same concept (synonymy). Most of information retrieval frameworks have a weakness to deal with the semantics of the words in term of (indexing, Boolean model, Latent Semantic Analysis (LSA) , Latent semantic Index (LSI) and semantic ranking, etc.). Traditional Arabic Information Retrieval (AIR) models performance insufficient with semantic queries, which deal with not only the keywords but also with the context of these keywords. Therefore, there is a need for a semantic information retrieval model with a semantic index structure and ranking algorithm based on semantic index.