Integrated-Block: A New Combination Model to Improve Web Page Segmentation




Webpage analysis, webpage segmentation, semantic text similarity, Gestalt Law of grouping


Context: Web page segmentation methods have been used for different purposes such as web page classification and content analysis. These methods categorize a web page into different blocks, where each block contains similar components.

Objective: The goal of this paper is to propose a new segmentation approach that semantically segments web pages into integrated blocks and obtains high segmentation accuracy.

Method: In this paper, we propose a new segmentation model that semantically segments web pages into integrated blocks, where (1) it merges web page content into basic-blocks by simulating human perception using Gestalt laws of grouping; and, (2) it utilizes semantic text similarity to identify similar blocks and regroup these similar basic-blocks as integrated blocks.

Results: To verify the accuracy of our approach, we (1) applied it to three datasets, (2) compared it with the five existing state-of-the-art algorithms. The results show that our approach outperforms all the five comparison methods in terms of precision, recall, F-1 score, and ARI.

Conclusion: In this paper, we propose a new segmentation model and apply it to three datasets to (1) generate basic-blocks by simulating human perception to segment a web page, (2) identify semantically related blocks and regroup them as an integrated block, and (3) address limitations found in existing approaches.


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Author Biographies

Saeedeh Sadat Sajjadi Ghaemmaghami, University of Alberta, Canada

Saeedeh Sadat Sajjadi Ghaemmaghami received the Ph.D. degree in computer engineering from the University of Alberta in 2021. Her research interests include web page analysis, machine learning, natural language processing, pattern recognition, and data mining.

James Miller, University of Alberta, Canada

James Miller, P.Eng (Alberta) has been a full professor with the Dept. Electrical and Computer Engineering at The University of Alberta since 2000. Previously, he was a professor at the University of Strathclyde (U.K.) and a principal research scientist at the National Electronics Research Initiative (U.K.). He has been an active researcher for more than thirty years across a wide range of topics, ranging from Computer Vision, Pattern Recognition, Embedded System Design, Software Engineering, Web Engineering and Proactive Analytics. He has published more than 100 articles in peer-reviewed journals including many IEEE and ACM venues.


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How to Cite

Ghaemmaghami, S. S. S. ., & Miller, J. . (2022). Integrated-Block: A New Combination Model to Improve Web Page Segmentation. Journal of Web Engineering, 21(04), 1103–1144.