Abstract
Web page, a kind of semi-structured document, includes a lot of additional attribute content besides text information. Traditional web page classification technology is mostly based on text classification methods. They ignore the additional attribute information of web page text. We propose WEB-GNN, an approach for Web page classification. There are two major contributions to this work. First, we propose a web page graph representation method called W2G that reconstructs text nodes into graph representation based on text visual association relationship and DOM-tree hierarchy relationship and realizes the efficient integration of web page content and structure. Our second contribution is to propose a web page classification method based on graph convolutional neural network. It takes the web page graph representation as to the input, integrates text features and structure features through graph convolution layer, and generates the advanced webpage feature representation. Experimental results on the Web-black dataset suggest that the proposed method significantly outperforms text-only method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davison, X.Q., Davison., B.: Web page classification: features and algorithms. ACM Comput. Surv. (CSUR) 41(2), 1–31 (2009)
Deng, L., Du, X., Shen, J.: Web page classification based on heterogeneous features and a combination of multiple classifiers. Front. Inf. Technol. Electron. Eng. 21, 1004–995 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Gao, H., Ji, S.: Graph u-nets. In: international conference on machine learning, pp. 2083–2092. PMLR (2019)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
Golub, K., Ardö, A.: Importance of HTML Structural Elements and Metadata in Automated Subject Classification. In: Rauber, A., Christodoulakis, S., Tjoa, A.M. (eds.) Research and Advanced Technology for Digital Libraries, ECDL 2005, Lecture Notes in Computer Science, vol. 3652, pp. 368–378. Springer, Berlin (2005)
Hamaguchi, T., Oiwa, H., Shimbo, M., Matsumoto, Y.: Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach. arXiv preprint arXiv:1706.05674 (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216 (2017)
Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 562–570 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kovacevic, M., Diligenti, M., Gori, M., Milutinovic, V.: Visual adjacency multigraphs-a novel approach for a web page classification. In: Proceedings of SAWM04 workshop, ECML 2004, (2004)
Lewis, D.D., Ringuette, M.: A comparison of two learning algorithms for text categorization. In: Third Annual Symposium on Document Analysis and Information Retrieval, vol. 33, pp. 81–93 (1994)
Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101 (2016)
Lu, M.Y., Shen, D., Guo, C.H., Lu, Y.C.: Web-page summarization methods for web-page classification. Dianzi Xuebao (Acta Electronica Sinica) 34(8), 1475–1480 (2006)
Mitchell, T.: Machine Learning, Mcgraw-hill Higher Education, New York (1997)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009). https://doi.org/10.1109/TNN.2008.2005605
Shanks, V., Williams, H.: Fast categorisation of large document collections. In: String Processing and Information Retrieval, International Symposium on, pp. 0194–0194. IEEE Computer Society (2001)
Song, Y., Shi, S., Li, J., Zhang, H.: Directional skip-gram: Explicitly distinguishing left and right context for word embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 2 (Short Papers), pp. 175–180 (2018)
Sun, A., Lim, E.P., Ng, W.K.: Web classification using support vector machine. In: Proceedings of the 4th International Workshop on Web Information and Data Management, pp. 96–99 (2002)
Wiener, E., Pedersen, J.O., Weigend, A.S.: A neural network approach to topic spotting. In: Proceedings of SDAIR-95, 4th Annual Symposium on Document Analysis and Information Retrieval, vol. 317, p. 332. Las Vegas, NV (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, T., Cui, B. (2022). Web Page Classification Based on Graph Neural Network. In: Barolli, L., Yim, K., Chen, HC. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2021. Lecture Notes in Networks and Systems, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-79728-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-79728-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79727-0
Online ISBN: 978-3-030-79728-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)