of extraction rules that describe how to locate the desired information on a Web page. The wrapper verification system uses the functioning wrapper to collect extracted data. It then learns patterns describing the structure of data. These patterns are used to verify that the wrapper is correctly extracting data at a later date. If a change is detected, the system can automatically repair a wrapper by using this structural information to locate examples of data on the new pages and re-running the wrapper induction system with these examples. At the core of these wrapper maintenance applications is a machine learning algorithm that learns structural information about common data fields. In this paper we introduce the algorithm, DataProG**, and describe its application to the wrapper maintenance tasks in** detail. Though we focus on web applications, the learning technique is not web-specific, and can be used for data validation in general.

Note that we distinguish two types of extraction rules: landmark-based rules that extract data by exploiting the structure of the Web page, and content-based rules, which we refer to as content patterns or simply patterns, that exploit the structure of the field itself. Our previous work focused on learning landmark rules for information extraction (Muslea, Minton, & Knoblock, 2001). The current work shows that augmenting these rules with content-based patterns provides a foundation for sophisticated wrapper maintenance applications.

## 2. Learning Content Patterns

The goal of our research is to extract information from semi-structured information sources. This typically involves identifying small chunks of highly **informative data on formatted**
pages (as opposed to parsing natural language text). Either **by convention or design, these**
fields **are usually structured: phone numbers, prices, dates, street addresses, names, schedules,** *etc.* **Several examples of street addresses are given in Fig. 2. Clearly, these strings**
are not arbitrary, but share some similarities. The objective of our work is to learn the structure of such fields.

4676 Admiralty Way 10924 Pico Boulevard 512 Oak Street 2431 Main Street 5257 Adams Boulevard Figure 2: Examples of a street address field

## 2.1 Data Representation

In previous work, researchers described the fields extracted **from Web pages by a characterlevel grammar (Goan, Benson, & Etzioni, 1996) or a collection of global features, such as the**
number of words and the density of numeric characters (Kushmerick, 1999). We employ an intermediate word-level representation that balances the descriptive power and specificity of the character-level representation with the compactness **and computational efficiency of** the global representation. Words, or more accurately tokens, are strings generated from