Wrapper induction: Efficiency and expressiveness

By Nicholas Kushmerick. Artificial Intelligence, 2000

656 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/2f052f40a3307de1e45e11a3007a7552b36ebfc8

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Citations in the corpus

E-commerce and tourism (2004, background): “Such techniques provide highly accurate extraction rules that adapt to structural site changes, ensuring the correct extraction of data [5, 6].”
WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021, methodology): “Previous studies mainly focus on building templates for HTML DOM tree, called Wrapper Induction (Kushmerick, 2000; Flesca et al., 2004; Kushmer-ick et al., 1997; Muslea et al., 1999), or using well designed visual features like font sizes, element sizes, and positions (Zhu et al., 2005, 2006).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “Rule-based approaches [89] are the general solutions for NER.”
A flexible learning system for wrapping tables and lists in HTML documents (2002, background): “Lesion studies show that the more exotic builders do indeed improve performance on complex wrapper-learning tasks, and experiments on artificial data suggest that the system has broader coverage and a faster learning rate than two earlier wrapper-learning systems, WEIN [ 16 ] and STALKER [21, 22].”
Web Data Extraction, Applications and Techniques: A Survey (2012, methodology): “One of the early approaches is WIEN [78].”
Web data extraction based on partial tree alignment (2005, methodology): “Example wrapper induction systems include WIEN [19], Softmealy [18], Stalker [23], WL(2) [11], [25], etc.”
ViDE: A Vision-Based Approach for Deep Web Data Extraction (2010, background): “The former, such as WIEN [15], Soft- Mealy [12], and Stalker [22], represents documents as sequences of tokens or characters, and generates delimiterbased extraction rules through a set of training examples.”
Active Hidden Markov Models for Information Extraction (2001, methodology): “While first IE algorithms were hand-crafted sets of rules (e.g., [7]), researchers soon turned to learning extraction rules from hand-labeled documents (e.g., [9, 11 ]).”
Semantic Annotation, Indexing, and Retrieval (2003, methodology): “The main drawback of using automated methods like IE [10], [29], and wrapper induction [31], without specific modifications for the Semantic Web, is that none of these approaches expects an input or produces output with respect to ontologies.”
Context in Web Search. (2000, methodology): “Many methods have been proposed for such information extraction, see for example [2, 9, 20, 38, 39, 40, 58, 59].”
RoadRunner: Towards Automatic Data Extraction from Large Web Sites (2001, background): “Figure 6 actually contains two tables; while Table A refers to experiments we have conducted independently, in Table B we compare for 5 page classes our results with those of other data extraction systems for which experimental results are available in the literature, namely Wien [13, 12] and Stalke”
Mining data records in Web pages (2003, methodology): “[4][8][9][12] use the machine learning approach, which is semiautomatic as it requires human labeling of specific regions in the Web page to mark them as interesting.”
Crowdsourcing large scale wrapper inference (2014, background): “, [24] and Lixto [16]) rely on a user providing feedback in the form of labeled values guiding the inference towards accurate wrappers.”
Joint Inference in Information Extraction (2007, background): “Detecting these is key for information extraction, and a number of approaches use rules designed specifically for this purpose (e.g., Kushmerick (2000)).”
Potter's Wheel: An Interactive Data Cleaning System (2001, background): “There have also been some learning-based approaches for automatic text wrapping and segmentation [15, 4].”
Automatic Wrappers for Large Scale Web Extraction (2011, methodology): “[15, 14] and the xpathbased method from Dalvi et al.”
KIM – a semantic platform for information extraction and retrieval (2004, background): “2001; Ciravegna, Dingli, Petrelli and Wilks 2002) and wrapper induction (Kushmerick 2000), without speciﬁc modiﬁcations for the Semantic Web, is that none of these approaches expect input or produce output with respect to ontologies.”