الفهرس 
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Abstract
We are in the Big Data era. The rapid growth of the Internet and its applications make data grow to huge volumes. So, today most users need search engines to facilitate search and information retrieval processes by using some of data mining tools. Unfortunately, traditional search engines usually return long lists of results, which may take up a lot of time to browse until finding the exact solution, if found at all. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and its semantic relationship. Using external knowledge bases for such similarity measures is a growing field of research, due to their rich content and semantic relations. This thesis aims to creat a semantic domain-specific graph of keywords using data extracted from different knowledge sources including the internet. Therefore, this thesis proposes a search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The proposed framework combines some modern technologies such as Extraction, Transformation and Integration (ETI) processes, ontology graph, and indexing using wide column of Not Only SQL (NoSQL) technique. Several experiments were conducted to measure the efficiency of the proposed system using two data sources DBpedia and YAGO datasets. Executed test cases have achieved a precision rate of 97% and a recall rate of 94% with appropriate response time compared to the relevant systems.