الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 235 |  |  |
| | | from | 235 | | |
Abstract
Arabic Language is the mother tongue for 23 countries and more than 350 million persons. It is the language of the Holy Quran; therefore, many non-Arabic Islamic countries, like Pakistan, teach Arabic as a second language. Nevertheless, it is obvious that the Arabic content on the Web is less than what should be. Moreover, the evolution of the Semantic Web (SW) technology added a new dimension to the problem. That is mainly owing to the scarcity of tools that can support the Arabic script. Furthermore, the Arabic resources, if available, are not free. In addition, there are many technical problems in the semantic dealing with the Arabic context. Therefore, most of the developed applications are not sufficiently competent. However, due to the significance of the Arabic Language, it is inevitable to overcome these deficiencies in order to put the Arabic Language in the category of the machine-semantically-interpretable languages, rather than just the textually processable ones. This way, we can exploit the power of the Semantic Web features in extracting the essence of the knowledge residing in the Arabic web documents and going beyond dealing with its rigid texts. In fact, one of the main motives of the Semantic Web is to improve the retrieval performance of the search systems. The semantic search systems, unlike keyword-based ones, aim to discover pages related to the query’s concepts rather than merely collecting all pages instantiating its keywords. To that end, the concepts must be defined to be used as a semantic index instead of the traditional lexical one. In this research, as a step in the pathway for fully supporting the Arabic Language semantically, we proposed an Arabic Semantic Search System. The proposed system is based on the Vector Space Model (VSM) since it is one of the most common information retrieval models due to its capability of expressing the documents’ structure. In fact, like all keyword-based search systems, the sensitivity of the traditional VSM to the query’s keywords reduces its retrieval effectiveness. Therefore, the proposed system exploited the Universal Word Net ontology for producing an Arabic Concept-Space in order to be used as the index of an enhanced Semantic Vector Space Model instead of its traditional Term-Space. The system is applied on a full dump of the Arabic Wikipedia, and it allowed the documents to be represented by their topic, and thus be classified semantically. This, consequently, enhances the retrieval effectiveness of the search system.