Similarity based Dynamic Web Data Extraction and Integration System from Search Engine Result Pages for Web Content Mining
This addresses the problem of information overload for web users by improving data extraction from SERPs, though it appears incremental as it builds on existing methods like DEPTA.
The paper tackles the challenge of extracting and integrating information from search engine result pages (SERPs) for web content mining by proposing two similarity-based mechanisms, WDES and WDICS, which outperform DEPTA in terms of precision and recall.
There is an explosive growth of information in the World Wide Web thus posing a challenge to Web users to extract essential knowledge from the Web. Search engines help us to narrow down the search in the form of Search Engine Result Pages (SERP). Web Content Mining is one of the techniques that help users to extract useful information from these SERPs. In this paper, we propose two similarity based mechanisms; WDES, to extract desired SERPs and store them in the local depository for offline browsing and WDICS, to integrate the requested contents and enable the user to perform the intended analysis and extract the desired information. Our experimental results show that WDES and WDICS outperform DEPTA [1] in terms of Precision and Recall.