An increasing number of data sources now become available on the Web, but often their contents are only accessible through query interfaces. For a domain of interest, there often exist many such sources with varied coverage or querying capabilities. As an important step to the integration of these sources, we consider the integration of their query interfaces. More specifically, we focus on the crucial step of the integration: accurately matching the interfaces. While the integration of query interfaces has received more attentions recently, current approaches are not sufficiently general: (a) they all model interfaces with flat schemas; (b) most of them only consider 1:1 mappings of fields over the interfaces; (c) they all perform the integration in a blackbox-like fashion and the whole process has to be restarted from scratch if anything goes wrong; and (d) they often require laborious parameter tuning. In this paper, we propose an interactive, clustering-based approach to matching query interfaces. The hierarchical nature of interfaces is captured with ordered trees. Varied types of complex mappings of fields are examined and several approaches are proposed to effectively identify these mappings. We put the human integrator back in the loop and propose several novel approaches to the interactive learning of parameters and the resolution of uncertain mappings. Extensive experiments are conducted and results show that our approach is highly effective. 1.

More and more databases are becoming Web accessible through form-based search interfaces, and many of these sources are E-commerce sites. Providing a unified access to multiple Ecommerce search engines selling similar products is of great importance in allowing users to search and compare products from multiple sites with ease. One key task for providing such a capability is to integrate the Web interfaces of these Ecommerce search engines so that user queries can be submitted against the integrated interface. Currently, integrating such search interfaces is carried out either manually or semi-automatically, which is inefficient and difficult to maintain. In this paper, we present WISE-Integrator- a tool that performs automatic integration of Web Interfaces of Search Engines. WISE-Integrator employs sophisticated techniques to identify matching attributes from different search interfaces for integration. It also resolves domain differences of matching attributes. Our experimental results based on 20 and 50 interfaces in two different domains indicate that WISE-Integrator can achieve high attribute matching accuracy and can produce high-quality integrated search interfaces without human interactions. 1.

Recently, the Web has been rapidly “deepened ” by many searchable databases online, where data are hidden behind query forms. For modelling and integrating Web databases, the very first challenge is to understand what a query interface says – or what query capabilities a source supports. Such automatic extraction of interface semantics is challenging, as query forms are created autonomously. Our approach builds on the observation that, across myriad sources, query forms seem to reveal some “concerted structure, ” by sharing common building blocks. Toward this insight, we hypothesize the existence of a hidden syntax that guides the creation of query interfaces, albeit from different sources. This hypothesis effectively transforms query interfaces into a visual language with a non-prescribed grammar – and, thus, their semantic understanding a parsing problem. Such a paradigm enables principled solutions for both declaratively representing common patterns, by a derived grammar, and systematically interpreting query forms, by a global parsing mechanism. To realize this paradigm, we must address the challenges of a hypothetical syntax – that it is to be derived, and that it is secondary to the input. At the heart of our form extractor, we thus develop a 2P grammar and a best-effort parser, which together realize a parsing mechanism for a hypothetical syntax. Our experiments show the promise of this approach – it achieves above 85 % accuracy for extracting query conditions across random sources. 1.

The contents of many valuable web-accessible databases are only available through search interfaces and are hence invisible to traditional web “crawlers. ” Recently, commercial web sites have started to manually organize web-accessible databases into Yahoo!-like hierarchical classification schemes. Here, we introduce QProber, a modular system that automates this classification process by using a small number of query probes, generated by document classifiers. QProber can use a variety of types of classifiers to generate the probes. To classify a database, QProber does not retrieve or inspect any documents or pages from the database, but rather just exploits the number of matches that each query probe generates at the database in question. We have conducted an extensive experimental evaluation of QProber over collections of real documents, experimenting with different types of document classifiers and retrieval models. We have also tested our system with over one hundred web-accessible databases. Our experiments show that our system has low overhead and achieves high classification accuracy across a variety of databases.

The Web has been rapidly “deepened ” by the prevalence of databases online. With the potentially unlimited information hidden behind their query interfaces, this “deep Web ” of searchable databases is clearly an important frontier for data access. This paper surveys this relatively unexplored frontier, measuring characteristics pertinent to both exploring and integrating structured Web sources. On one hand, our “macro ” study surveys the deep Web at large, in April 2004, adopting the random IP-sampling approach, with one million samples. (How large is the deep Web? How is it covered by current directory services?) On the other hand, our “micro ” study surveys source-specific characteristics over 441 sources in eight representative domains, in December 2002. (How “hidden ” are deep-Web sources? How do search engines cover their data? How complex and expressive are query forms?) We report our observations and publish the resulting datasets to the research community. We conclude with several implications (of our own) which, while necessarily subjective, might help shape research directions and solutions. 1.

Data on the Web in HTML tables is mostly structured, but we usually do not know the structure in advance. Thus, we cannot directly query for data of interest. We propose a solution to this problem based on document-independent extraction ontologies. The solution entails elements of table understanding, data integration, and wrapper creation. Table understanding allows us to recognize attributes and values, pair attributes with values, and form records. Data-integration techniques allow us to match source records with a target schema. Ontologically specified wrappers allow us to extract data from source records into a target schema. Experimental results show that we can successfully map data of interest from source HTML tables with unknown structure to a given target database schema. We can thus “directly” query source data with unknown structure through a known target schema. 1

Although originally designed for large-scale electronic publishing, XML plays an increasingly important role in the exchange of data on the Web. In fact, it is expected that XML will become the lingua franca of the Web, eventually replacing HTML. Not surprisingly, there has been a great deal of interest on XML both in industry and in academia. Nevertheless, to date no comprehensive study on the XML Web (i.e., the subset of the Web made of XML documents only) nor on its contents has been made. This paper is the first attempt at describing the XML Web and the documents contained in it. Our results are drawn from a sample of a repository of the publicly available XML documents on the Web, consisting of about 200,000 documents. Our results show that, despite its short history, XML already permeates the Web, both in terms of generic domains and geographically. Also, our results about the contents of the XML Web provide valuable input for the design of algorithms, tools and systems that use XML in one form or another.

An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites. These pages are often referred to as the Hidden Web or the Deep Web. Since there are no static links to the Hidden Web pages, search engines cannot discover and index such pages and thus do not return them in the results. However, according to recent studies, the content provided by many Hidden Web sites is often of very high quality and can be extremely valuable to many users. In this paper, we study how we can build an effective Hidden Web crawler that can autonomously discover and download pages from the Hidden Web. Since the only “entry point ” to a Hidden Web site is a query interface, the main challenge that a Hidden Web crawler has to face is how to automatically generate meaningful queries to issue to the site. Here, we provide a theoretical framework to investigate the query generation problem for the Hidden Web and we propose effective policies for generating queries automatically. Our policies proceed iteratively, issuing a different query in every iteration. We experimentally evaluate the effectiveness of these policies on 4 real Hidden Web sites and our results are very promising. For instance, in one experiment, one of our policies downloaded more than 90 % of a Hidden Web site (that contains 14 million documents) after issuing fewer than 100 queries.

Existing Internet search engines use web crawlers to download data from the Web. Page quality is measured on central servers, where user queries are also processed. This paper argues that using crawlers has a list of disadvantages. Most importantly, crawlers do not scale. Even Google, the leading search engine, indexes less than 1% of the entire Web. This paper proposes a distributed search engine framework, in which every web server answers queries over its own data. Results from multiple web servers will be merged to generate a ranked hyperlink list on the submitting server. This paper presents a series of algorithms that compute PageRank in such framework. The preliminary experiments on a real data set demonstrate that the system achieves comparable accuracy on PageRank vectors to Google's wellknown PageRank algorithm and, therefore, high quality of query results.

A wealth of information is hidden within unstructured text. This information is often best exploited in structured or relational form, which is suited for sophisticated query processing, for integration with relational databases, and for data mining. Current information extraction techniques extract relations from a text database by examining every document in the database, or use filters to select promising documents for extraction. The exhaustive scanning approach is not practical or even feasible for large databases, and the current filtering techniques require human involvement to maintain and to adopt to new databases and domains. In this paper, we develop an automatic query-based technique to retrieve documents useful for the extraction of user-defined relations from large text databases, which can be adapted to new domains, databases, or target relations with minimal human effort. We report a thorough experimental evaluation over a large newspaper archive that shows that we significantly improve the efficiency of the extraction process by focusing only on promising documents.