In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). There are several approaches in query expansion field such as statistical approach. This approach depends on term frequency to generate expansion features; nevertheless it does not consider meaning or term dependency. In addition, there are other approaches such as semantic approach which depends on a knowledge base that has a limited number of terms and relations. In this paper, researchers propose a hybrid approach for query expansion which utilizes both statistical and semantic approach. To select the optimal terms for query expansion, researchers propose an effective weighting method based on particle swarm optimization (PSO). A system prototype was implemented as a proof-of-concept, and its accuracy was evaluated. The experimental was carried out based on real dataset. The experimental results confirm that the proposed approach enhances the accuracy of query expansion.

中文翻译：

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阿拉伯语信息检索的混合语义查询扩展方法

实际上，大多数信息检索系统都是基于关键字匹配来检索文档的，但是在检索具有语法上不同的关键字（形式）具有相似含义的文档时，肯定会失败。克服此限制的一种众所周知的方法是查询扩展（QE）。查询扩展领域有几种方法，例如统计方法。这种方法取决于词频来生成扩展特征。但是，它不考虑含义或术语依赖性。另外，还有其他方法，例如语义方法，它依赖于术语和关系数量有限的知识库。在本文中，研究人员提出了一种使用统计和语义方法的混合查询扩展方法。要为查询扩展选择最佳条件，研究人员提出了一种基于粒子群优化（PSO）的有效加权方法。系统原型被实现为概念证明，并对其准确性进行了评估。实验是基于真实数据集进行的。实验结果证明，该方法提高了查询扩展的准确性。