While much of the data on the web is unstructured in nature, there is also a significant amount of embedded structured data, such as product information on e-commerce sites or stock data on financial sites. A large amount of research has focused on the problem of generating wrappers, i.e., software tools that allow easy and robust extraction of structured data from text and HTML sources. In many applications, such as comparison shopping, data has to be extracted from many different sources, making manual coding of a wrapper for each source impractical. On the other hand, fully automatic approaches are often not reliable enough, resulting in low quality of the extracted data. We describe a complete system for semi-automatic wrapper generation that can be trained on different data sources in a simple interactive manner. Our goal is to minimize the amount of user effort for training reliable wrappers through design of a suitable training interface that is implemented based on a powerful underlying extraction language and a set of training and ranking algorithms. Our experiments show that our system achieves reliable extraction with a very small amount of user effort.
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