Fundamentals of Web Data Extraction Software

A web data extraction software is a software that automatically and repeatedly extracts data from web pages with changing content and delivers the extracted data to a database or some other application. The task of web data extraction performed by such a system is usually divided into five different functions:

  • (1) web interaction, which comprises mainly the navigation to pre-determined target web pages containing the desired information;
  • (2) support for wrapper generation and execution, where a wrapper is a program that identifies the desired data on target pages, extracts the data and transforms it into a structured format;
  • (3) scheduling, which allows repeated application of previously generated wrappers to their respective target pages;
  • (4) data transformation, which includes filtering, transforming, refining, and integrating data extracted from one or more sources and structuring the result according to a desired output format (usually XML or relational tables);
  • (5) delivering the resulting structured data to external applications such as database management systems, data warehouses, business software systems, content management systems, decision support systems, RSS publishers, email servers, or SMS servers. Alternatively, the output can be used to generate new web services out of existing and continually changing web sources.

A typical, web scraping project is a set of page objects or templates that define how data is extracted from one type of webpage. For example, if you were extracting data from a product catalogue, the product detail pages would be defined by one template. A webpage listing all products in a category would be defined by another template.

Formal foundations and semantics of data extraction.

There are four main formal approaches to define (the semantics of) web wrappers:

In the functional approach, a web wrapper is seen as a mapping f from the parse or DOM tree T of a web page to a set f(T) of sub-trees of T, where each sub-tree corresponds to an extracted data object. Another step specifies the relabling of extracted data to fit a previously defined output schema.

The logical approach (see web data extraction: logical fundamentals) consists of the specification of a finite number of monadic predicates, i.e., predicates, that define sets of parse-tree nodes and that evaluate to true or false on each node of a tree-structured document. The predicates can be defined either by logical formulas, or by logic programs such as monadic datalog, which was shown to be equivalent in expressive power to monadic second-order logic (MSO). Note that languages such as XPATH or regular path expressions are subsumed by MSO.

The automata theoretic approach relies on tree automata, which are a generalization of finite state automata from words to trees. The equivalence of the two logical approaches, MSO and monadic datalog, and the unranked query automata approach shows that the class of wrappers definable in these formalisms is quite natural and robust. Fortunately, this class is also quite expressive, as it accommodates most of the relevant data extraction tasks and properly contains the wrappers definable in some specifically designed wrapper programming languages.

Finally, the textual approach interprets web pages as text strings and uses string pattern matching for data extraction.

Methods of wrapper generation

With regard to user involvement, three principal approaches for wrapper generation can be distinguished:

(1) Manual wrapper programming, in which the system merely supports a user in writing a specific wrapper but cannot make any generalizations from the examples provided by the user; (2) wrapper induction, where the user provides examples and counterexamples of instances of extraction patterns, and the system induces a suitable wrapper using machine learning techniques; and (3) semi-automatic interactive wrapper generation, where the wrapper designer not only provides example data for the system, but rather accompanies the wrapper generation in a systematic computer-supported interactive process involving generalization, correction, testing, and visual programming techniques.

Wrapper generator

The wrapper generator supports the user during the wrapper design phase. It commonly has a visual interface that allows the user to define which data should be extracted from web pages and how this data should be mapped into a structured format such as XML. The visual interface itself can include several windows, such as: (i) a browser window that renders example web pages for the user; (ii) a parse tree window that shows the HTML parse tree of the current web page rendered by the browser; (iii) a control window that allows the user to view and control the overall progress of the wrapper design process and to input textual data (e.g. attribute names or XML tags); and (iv) a program window that displays the wrapper program constructed so far and allows the user to further adjust or correct the program.

The subunit that actually generates the wrapper is contained in what is here referred to as the program generator. This submodule interprets the user actions on the example web pages and successively generates the wrapper. Semi-automatic wrapper generators allow the user to either specify the URL of example web pages, which are structurally similar to the target web pages, or to navigate to such example pages. In the latter case, the navigation is recorded and can be automatically reproduced. The example pages are rendered in a browser window. Most wrapper generators also display the parse tree of the current page. In most systems, the wrapper designer can click on a node of the HTML parse tree, and the system immediately highlights the corresponding part of the example page in the browser, and vice versa. The designer can, thus, iteratively narrow down an example of a relevant data item after a few trials. Many web data extraction systems exhibit an XPATH-like path expression that precisely identifies the selected data item. The wrapper designer can then generalize this path expression by replacing some of the elements with wildcards. The result is a generalized pattern that matches several similar data items on similarly structured pages. Some systems (e.g. Data Toolbar ) allow a wrapper designer to select data items directly on the rendered web page. The wrapper generator often also provides support for associating a name or tag to each extraction pattern and for organizing patterns hierarchically. Systems based on wrapper induction allow the user to supply a large number of sample web pages with positive and/or negative examples of desired data items, from which the program generator tries to learn a generalized pattern. Systems for manual wrapper programming simply provide a visual environment for developing a wrapper in the underlying wrapper programming language, for testing the wrapper against example pages and for debugging. In addition, the visual interface usually allows an application designer to specify when a wrapper should be executed.

Wrapper executor

The wrapper executor is the engine that facilitates the deployment of the previously generated wrappers. Typically, a wrapper program comprises functions such as deep web navigation (e.g. the means to fill out forms), identification of relevant elements with declarative rules or procedural scripts, and generation of a structured output format (e.g. XML or relational tables). Typically, the wrapper executor can also receive additional input parameters such as a start URL, a predefined output structure (e.g. a particular XML schema), and special values for forms. In some approaches, the output includes metadata (e.g. verification alerts in case of violated integrity constraints) in addition to the actual data of interest. The wrapper repository stores the generated wrappers together with metadata. In some approaches, wrapper templates and libraries of domain concepts can be reused and extended. Systems such as Dapper5 offer a community-based extensible wrapper repository. In principle, systems can offer automatic wrapper maintenance and adaptation as part of the repository.

Data transformation and integration unit

The data transformation and integration unit provides a means to streamline the results of multiple wrappers into one harmonized homogeneous result. This step includes data integration, transformation and cleaning, basically functions commnly found in the Transform step of ETL processes of data warehouses and mediation systems. Technologies and tools comprise query languages, visual data mapping, automatic schema matching and de-duplication techniques. Additionally, the data can be composed into desired result formats such as HTML, Excel or PDF.

Data delivery unit

The data delivery unit (capturing the Load step in the ETL philosophy) decides about the appropriate output channel such as email, Open Database Connectivity (ODBC) connection, or FTP. Advanced wrapper generation systems offer a number of system connectors to state-of-the-art databases and enterprise software such as Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP), or partner with software providers in the area of Enterprise Application Integration (EAI) or Business Intelligence (BI) who offer such solutions.

Central control and scheduling unit

The central control and scheduling unit is the heart of the processing engine. In general, synchronous and asynchronous extraction processes can be distinguished. A typical example of an asynchronous triggering is a real-time user request in a flight meta-search application. The request is queued, the respective wrappers are triggered, parameter mappings are performed, and the result is integrated and presented to the user in real-time. Sophisticated approaches offer a workflow-based execution paradigm, including parallelism, returning partial results, and interception points in complex booking transactions. On the other hand, market monitoring or web process integration are typical synchronous scenarios. An intelligent scheduler is responsible for distributing requests to the wrapper executor, iterating over a number of possible form inputs, and dealing with exception handling.

Web data extraction applications
Web market monitoring

Nowadays, a lot of basic information about competitors can be retrieved legally from public information sources on the Web, such as annual reports, press releases or public data bases. On the one hand, powerful and efficient tools for Extracting, Transforming and Loading (ETL) data from internal sources and applications into a Business Intelligence (BI) data warehouse are already available and largely employed. On the other hand, there is a growing economic need to efficiently integrate external data, such as market and competitor information, into these systems as well. With the World Wide Web as the largest single database on earth, advanced data extraction and information integration techniques as described in this paper are required to process this web data automatically. At the same time, the extracted data has to be cleaned and transformed into semantically useful formats and delivered in a “web-ETL” process into a BI system. Key factors in this application area include scalable environments to extract and schedule processing of very large data sets efficiently, capabilities to pick representative data samples, cleaning extracted data to make it comparable, and connectivity to data warehouses.

Web process integration

In markets such as the automotive industry, business processes are largely carried out by means of web portals. Business critical data from various divisions such as quality management, marketing and sales, engineering, procurement and supply chain management have to be manually gathered from web portals. Through automation, suppliers can dramatically reduce the cost while at the same time improving speed and reliability of these processes. Additionally, the automation means to leverage web applications to web services, and hence wrapper generation systems can be considered as an enabling technology for Service Oriented Architectures (SOA) as envisioned and realized in Enterprise Application Integration (EAI) and B2B processes. Key factors in this application area are workflow capabilities for the whole process of data extraction, transformation and delivery, capabilities to treat all kinds of special cases occurring in web interactions, and excellent support of the latest web standards used during secure transactions.


Increasingly, leading software vendors have started to provide mashup platforms such as Yahoo! Pipes or IBM QEDWiki. A mashup is a web application that combines a number of different websites into an integrated view. Usually, the content is taken via APIs by embedding RSS or atom feeds similar to REST (Representational State Transfer). In this context, wrapper technology transforms legacy web applications to light-weight APIs that can be integrated in mashups in the same way. As a result, web mashup solutions no longer need to rely on APIs offered by the providers of websites, but can rather extend the scope to the whole Web. Key factors for this application scenario include efficient real-time extraction capabilities for a large number of concurrent queries and detailed understanding of how to map queries to particular web forms.

Generic web wrapping

Generic web wrapping. Without explicit semantic annotations on the current Web (compare Semantic Web), data extraction systems that allow general web wrapping will have to move towards fully automatic wrapping of the existing World Wide Web. Important research challenges are (i) how to optimally bring semantic knowledge into the extraction process, (ii) how to make the extraction process robust (in particular, how to deal with false positives), and (iii) how to deal with the immense scaling issues. Today's web harvesting and automated information extraction systems show much progress, but still lack the combined recall and precision necessary to allow for very robust queries.

A related topic is that of auto-adapting wrappers. Most wrappers today depend on the tree structure of a given web page and suffer from failure when the layout and code of web pages change. Auto-adapting wrappers, which are robust against such changes, could use existing knowledge of the relations on previous versions of web page in order to automatically “heal” and adapt the extraction rules to the new format. The question here is how to formally capture change actions and execute the appropriate repair actions.

Wrapping from visual layouts

Whereas web wrappers today dominantly focus on either the flat HTML code or the DOM tree representation of web pages, recent approaches aim at extracting data from the CSS box model and, hence, the visual representation of web pages [5]. This method can be particularly useful for layout-oriented data structures such as web tables and allows to create automatic and domain-independent wrappers which are robust against changes of the HTML code implementation.

to formally capture change actions and execute the appropriate repair actions.

Data extraction from non-HTML file formats

There is a substantial interest from industry in wrapping documents in formats such as PDF and PostScript. Wrapping of such documents must be mainly guided by a visual reasoning process over white space and Gestalt theory, which is substantially different from web wrapping and will, hence, require new techniques and wrapping algorithms including concepts borrowed from the document understanding community.

Web interactions

Learning to deal with web interactions. As web pages are becoming increasingly dynamic and interactive, efficient wrapping languages have to make it possible to record, execute and generalize macros of web interactions and, hence, model the whole process of workflow integration. An example of such a web interactions is a complicated booking transaction.

Web form understanding and mapping

In order to automatically or interactively query deep web forms, wrappers have to learn the process of filling out complex web search forms and the usage of query interfaces. Such systems have to learn abstract representation for each search form and map them to a unified meta form and vice versa, taking into account different form element types, contents and labels.

Cross references

Web harvesting, web information extraction, data integration, logical fundamentals of web data extraction, snippet, service oriented architecture, datalog, data mining, wrapper, wrapper generator, wrapper induction, wrapper maintenance, wrapper stability, web services, business intelligence, enterprise application integration, XML, Xpath/Xquery, Semantic Web, ETL, screen scraper, data warehouse,

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The latest production build was released on 2020-03-04. Version 3.4 supports background data srcaping that does not distrupt other applications. Update your program for free if you own any of its previous versions. Check release history here.

Version 4.0, which is to be released in 2020, will significantly improve the performance and the flexibility of the DataTool by using the new data extraction engine based on CSS selectors.