A Survey of Web Information Extraction Systems

By Chia-Hui Chang, Mohammed Kayed, Moheb R. Girgis, Khaled Shaalan. IEEE Transactions on Knowledge and Data Engineering, 2006

927 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/3cc310717f422592c3cc9a046943777468c14358

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

Citations in the corpus

Semi-supervised multi-task learning of structured prediction models for web information extraction (2011, background): “[7, 31] present comprehensive surveys on existing techniques of web information extraction.”
Web Data Extraction, Applications and Techniques: A Survey (2012, methodology): “[19] introduced a tridimensional categorization of Web Data Extraction systems, based on task difficulties, techniques used and degree of automation.”
Exploring entrepreneurial legitimacy in reward-based crowdfunding (2014, background): “Web data extraction facilitates the collection of relatively large data-sets with high levels of data validity because the site is in active use and the data generated specifically for web-based transmission (Kosala and Blockeel 2000; Thelwall 2001; Chang et al. 2006).”
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems (2017, background): “has been a lot of work in IE on domains such as web documents (Chang et al., 2003; Etzioni et al., 2004; Cafarella et al., 2005; Chang et al., 2006; Banko et al., 2007; Etzioni et al., 2008; Mitchell et al., 2015) and scientific publication data (Shah et al.”
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey (2024, background): “2) Continual Learning (CL): CL aims to learn a sequence of new tasks over time within one single model, which has broad application in scenarios such as dialogue systems [100], information extraction systems [14], and question answering systems [201].”
Attention please!: learning analytics for visualization and recommendation (2011, background): “This is an excellent example of automatic metadata generation [6] or information extraction [5], an essential technology if we want to collect metadata of resources, activities and people at scale.”
ViDE: A Vision-Based Approach for Deep Web Data Extraction (2010, background): “and compared in [5], andwe do not discuss them any further here.”
Big data challenge: a data management perspective (2013, background): “Information extraction is necessary to structuralize the raw data so that they can be easily digested by analysis algorithms [2, 3].”
Crowdsourcing large scale wrapper inference (2014, neutral): “Wrapper induction for extracting data from web pages has been subject of many researches for more than fifteen years [6,13].”