Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.

In this paper we provide a fast, data-driven solution to the failing query problem: given a query that returns an empty answer, how can one relax the query's constraints so that it returns a non-empty set of tuples? We introduce a novel algorithm, loqr, which is designed to relax queries that are in the disjunctive normal form and contain a mixture of discrete and continuous attributes. loqr discovers the implicit relationships that exist among the various domain attributes and then uses this knowledge to relax the constraints from the failing query.In a first step, loqr uses a small, randomly-chosen subset of the target database to learn a set of decision rules that predict whether an attribute's value satisfies the constraints in the failing query; this query-driven operation is performed online for each failing query. In the second step, loqr uses nearest-neighbor techniques to find the learned rule that is the most similar to the failing query; then it uses the attributes' values from this rule to relax the failing query's constraints. Our experiments on six application domains show that loqr is both robust and fast: it successfully relaxes more than 95% of the failing queries, and it takes under a second for processing queries that consist of up to 20 attributes (larger queries of up to 93 attributes are processed in several seconds).

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to do this is to build specialized applications, which are time-consuming to develop and difficult to maintain. We have addressed this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a uniform representation that makes it simple and efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, databases, machine learning and automated planning. The resulting system, called Ariadne, makes it fast and easy to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.

Although much has been written about various information integration technologies, little has been said regarding how to combine these technologies together to build an entire application. We demonstrate TheaterLoc, an information integration application that allows users to retrieve information about theatres and restaurants for various U.S. cities, including an interactive map depicting their relative locations. The data retrieved by TheaterLoc comes from five distinct heterogeneous and distributed sources. The enabling technology used to achieve the integration includes: the Ariadne information mediator, a Web site wrapper learning tool, the Theseus execution system, and a mechanism for distributed spatial query planning. Our system is novel because it demonstrates how "virtual applications" can be rapidly built from a set of integration tools and existing online data sources.

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to achieve this integration is by building specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information mediators that extract, query, and integrate data from web sources. The resulting system, called Ariadne, makes it feasible to rapidly build information mediators that access existing web sources.