Khaled Shaalan

There is a growing interest in Arabic web content worldwide due to its importance for culture, religion, and economics. In the literature, researches that address searching Arabic web content using semantic web technology are still insufficient compared to Arabic’s actual importance as a language. In this research, we propose an Arabic semantic search approach that is applied on Arabic web content. This approach is based on the Vector Space Model (VSM). It uses the Universal WordNet ontology to build a rich concept-space index instead of the traditional term-space index. The proposed index is used for enhancing the capability of the semantic-based VSM. Moreover, the approach introduces a new incidence measurement to calculate the semantic significance degree of the document's concepts which is more suitable than the traditional term frequency measure. Furthermore, a novel method for calculating the semantic weight of the concept is introduced in order to determine the semantic similarity of two vectors. As a proof of concept, a system is applied on a full dump of the Arabic Wikipedia. The experimental results in terms of Precision, Recall and F-measure have showed improvement in performance from 77%, 56%, and 63% to 71%, 96%, and 81%, respectively.