Semantic Web and Web Page Clustering Algorithms: A Landscape View

The major evolution of the semantic web has become exchanging data between applications in all domains of activities. Based on this vision, different applications in recent days, e.g. in the fields of community web portals, social networking, e-learning, multimedia retrieval, etc. have been designed. Due to growing number of web services, clustering of web resources becomes a valuable tool for semantic web mining. Clustering of internet objects like Internet web pages’ intimate new methods for grouping correlated content for better understanding and satisfies massive user query results in web pages’ search. Hence, web pages clustering algorithms should be able to handle massive irregular content and discover knowledge regardless of the web page complexity. These algorithms vary depending on the characteristics and data types. So, choosing the most appropriate algorithm is not an easy process as it should be accurate in terms of time and space complexity. Therefore, this paper rigorously surveys the most important algorithms of different types used for web page clustering. In addition, a comparative analysis of all such algorithms are provided in terms of several parameters. Finally, a brief discussion is provided on why web page clustering is important in emerging era of Semantic Web of Thing (SWoT) applications. Copyright © 2020 Ahmed J. Obaid et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.