The extraction of multi-attribute objects from the deep web is the bridge between the unstructured web and structured data. Existing approaches either induce wrappers from a set of human-annotated pages or leverage repeated structures on the page without supervision. What the former lack in automation, the latter lack in accuracy. Thus accurate, automatic multi-attribute object extraction has remained an open challenge. AMBER overcomes both limitations through mutual supervision between the repeated structure and automatically produced annotations. Previous approaches based on automatic annotations have suffered from low quality due to the inherent noise in the annotations and have attempted to compensate by exploring multiple candidate wrappers. In contrast, AMBER compensates for this noise by integrating repeated structure analysis with annotation-based induction: The repeated structure limits the search space for wrapper induction, and conversely, annotations allow the repeated structure analysis to distinguish noise from relevant data. Both, low recall and low precision in the annotations are mitigated to achieve almost human quality (more than 98 percent) multi-attribute object extraction. To achieve this accuracy, AMBER needs to be trained once for an entire domain. AMBER bootstraps its training from a small, possibly noisy set of attribute instances and a few unannotated sites of the domain.
Besides , we are only aware of three other approaches @cite_28 @cite_0 @cite_34 that exploit the mutual benefit of unsupervised extraction and induction from automatic annotations. All these approaches are a form of learning, a concept well known in the machine learning community and that has already been successfully applied in the information extraction setting @cite_9 .
@cite_28 @cite_9 @cite_34 @cite_0
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We present a generic framework to make wrapper induction algorithms tolerant to noise in the training data. This enables us to learn wrappers in a completely unsupervised manner from automatically and cheaply obtained noisy training data, e.g., using dictionaries and regular expressions. By removing the site-level supervision that wrapper-based techniques require, we are able to perform information extraction at web-scale, with accuracy unattained with existing unsupervised extraction techniques. Our system is used in production at Yahoo! and powers live applications. Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional Information Extraction methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target relations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. The performance of SRES is further enhanced by classifying its output instances using the properties of the instances and the patterns. The features we use for classification and the trained classification model are independent from the target relation, which we demonstrate in a series of experiments. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which learns simpler pattern language than SRES. We present an original approach to the automatic induction of wrappers for sources of the hidden Web that does not need any human supervision. Our approach only needs domain knowledge expressed as a set of concept names and concept instances. There are two parts in extracting valuable data from hidden-Web sources: understanding the structure of a given HTML form and relating its fields to concepts of the domain, and understanding how resulting records are represented in an HTML result page. For the former problem, we use a combination of heuristics and of probing with domain instances; for the latter, we use a supervised machine learning technique adapted to tree-like information on an automatic, imperfect, and imprecise, annotation using the domain knowledge. We show experiments that demonstrate the validity and potential of the approach. We present in this paper a novel approach for extracting structured data from the Web, whose goal is to harvest real-world items from template-based HTML pages (the structured Web). It illustrates a two-phase querying of the Web, in which an intentional description of the data that is targeted is first provided, in a flexible and widely applicable manner. The extraction process leverages then both the input description and the source structure. Our approach is domain-independent, in the sense that it applies to any relation, either flat or nested, describing real-world items. Extensive experiments on five different domains and comparison with the main state of the art extraction systems from literature illustrate its flexibility and precision. We advocate via our technique that automatic extraction and integration of complex structured data can be done fast and effectively, when the redundancy of the Web meets knowledge over the to-be-extracted data.