Knowledge Based Deep Inception Model for Web Page Classification

Authors

  • Amit Gupta Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India https://orcid.org/0000-0002-2875-5216
  • Rajesh Bhatia Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2075

Abstract

Web Page Classification is decisive for information retrieval and management task and plays an imperative role for natural language processing (NLP) problems in web engineering. Traditional machine learning algorithms excerpt covet features from web pages whereas deep leaning algorithms crave features as the network goes deeper. Pre-trained models such as BERT attains remarkable achievement for text classification and continue to show state-ofthe-art results. Knowledge Graphs can provide rich structured factual information for better language modelling and representation. In this study, we proposed an ensemble Knowledge Based Deep Inception (KBDI) approach
for web page classification by learning bidirectional contextual representation using pre-trained BERT incorporating Knowledge Graph embeddings and fine-tune the target task by applying Deep Inception network utilizing parallel multi-scale semantics. Proposed ensemble evaluates the efficacy of fusing domain specific knowledge embeddings with the pre-trained BERT model. Experimental interpretation exhibit that the proposed BERT fused KBDI model outperforms benchmark baselines and achieve better performance in contrast to other conventional approaches evaluated on web page classification datasets.

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Published

2021-11-16

How to Cite

Gupta, A., & Bhatia, R. . (2021). Knowledge Based Deep Inception Model for Web Page Classification. Journal of Web Engineering, 20(07), 2131–2168. https://doi.org/10.13052/jwe1540-9589.2075

Issue

Section

SPECIAL ISSUE: ADVANCED PRACTICES IN WEB ENGINEERING 2021