Title: SEA²: semantic extractor, aligner and annotator - a framework for automatic deep web data extraction, alignment and annotation based on semantics

Authors: Umamageswari Baskaran; Kalpana Ramanujam

Addresses: Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India ' Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India

Abstract: Nowadays huge number of web databases is accessible through front-end search query forms. The data records returned are embedded within HTML templates and returned to the end-user in the form of web pages. These web pages are dynamically generated and are not indexed to search engines. Therefore, they are referred as deep web pages. They are intended for human understanding whereas they make automated processing difficult. In order to enable machine processing, as needed by many data analytics applications such as business intelligence, product intelligence, etc., the data records embedded in those deep web pages has to be extracted and annotated. This paper proposes an automated solution based on inferred semantic rules to perform extraction and annotation of structured data records from deep web pages. Experimental result shows that the use of domain knowledge in the form of inferred semantic rules improves the accuracy of deep web data extraction process.

Keywords: deep web; web database; HTML templates; web data extraction; annotation; server-side templates; DOM tree; semantic labelling; hidden web; surface web.

DOI: 10.1504/IJAIP.2024.140087

International Journal of Advanced Intelligence Paradigms, 2024 Vol.28 No.3/4, pp.221 - 237

Received: 13 Aug 2018
Accepted: 13 Nov 2018
Published online: 24 Jul 2024 *

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