Zahraa Chreim¹, Hussein Hazimeh^{1, 2}, Hassan Harb¹, Fouad Hannoun², Karl Daher², Elena Mugellini² and Omar Abou Khaled², ¹Lebanese University, Lebanon, ²University of Applied Sciences of Western Switzerland, Switzerland

Search engines are among the most popular web services on the World Wide Web. They facilitate the process of finding information using a query-result mechanism. However, results returned by search engines contain many duplications. In this paper, we introduce a new content-type-based similarity computation method to address this problem. Our approach divides the webpage into different types of content, such as title, subtitles, body, etc. Then, we find for each type a suitable similarity measure. Next, we add the different calculated similarity scores to get the final similarity score between the two documents, using a weighted formula. Finally, we suggest a new graph-based algorithm to cluster search results according to their similarity. We empirically evaluated our results with the Agglomerative Clustering, and we achieved about 61% reduction in web pages, 0.2757 Silhouette coefficient, 0.1269 Davies Bouldin Score, and 85 Calinski Harabasz Score.

Information Retrieval, Websites Similarity, Graph Representation, Similarity Measures, Graph Kernel, Deduplication, Search Engines.