Wrapper Maintenance: A Machine Learning Approach

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task

User driven information extraction with LODIE

Information Extraction (IE) is the technique for transforming unstructured or semi-structured data into structured representation that can be understood by machines. In this paper we use a user-driven Information Extraction technique to wrap entity-centric Web pages. The user can select concepts and properties of interest from available Linked Data. Given a number of websites containing pages about the concepts of interest, the method will exploit (i) recurrent structures in the Web pages and (ii) available knowledge in Linked data to extract the information of interest from the Web pages

CSM-399 - Providing Robust Access to Data in Web Pages

Much useful e-commerce information is available on web pages, especially those created by queries to web servers. The problem for programs to use that information is how to ‘screen-scrape’ the data off the web page into machineusable data structures. Wrappers for web data sources use knowledge of the page layout in order to extract data accurately. So they fail if page format changes. This paper describes a fast method for wrapper production and also a method to automatically detect page format change, before it causes data access to fail. The method works for pages that contain collections of items, such as lists, tables and hierarchical structures. It uses a representation of html documents, which makes repetitive features apparent. This provides fully automatic wrapper production for a class of web pages, and rapid interactive production for others

Conditional Random Fields for XML Applications

XML tree labeling is the problem of classifying elements in XML documents. It is a fundamental task for applications like XML transformation, schema matching, and information extraction. In this paper we propose XCRFs, conditional random fields for XML tree labeling. Dealing with trees often raises complexity problems. We describe optimization methods by means of constraints and combination techniques that allow XCRFs to be used in real tasks and in interactive machine learning programs. We show that domain knowledge in XML applications easily transfers in XCRFs thanks to constraints and combination of XCRFs. We describe an approach based on XCRF to learn tree transformations. The approach allows to solve xml data integration tasks and restructuration tasks. We have developed an open source toolbox for XCRFs. We use it to propose a Web service for the generation of personalized RSS feeds from HTML pages