No abstract

No abstract

The Web offers a corpus of over 100 million tables [6], but the meaning of each table is rarely explicit from the table itself. Header rows exist in few cases and even when they do, the attribute names are typically useless. We describe a system that attempts to recover the semantics of tables by enriching the table with additional annotations. Our annotations facilitate operations such as searching for tables and finding related tables.To recover semantics of tables, we leverage a database of class labels and relationships automatically extracted from the Web. The database of classes and relationships has very wide coverage, but is also noisy. We attach a class label to a column if a sufficient number of the values in the column are identified with that label in the database of class labels, and analogously for binary relationships. We describe a formal model for reasoning about when we have seen sufficient evidence for a label, and show that it performs substantially better than a simple majority scheme. We describe a set of experiments that illustrate the utility of the recovered semantics for table search and show that it performs substantially better than previous approaches. In addition, we characterize what fraction of tables on the Web can be annotated using our approach.

The Deep Web, i.e., content hidden behind HTML forms, has long been acknowledged as a significant gap in search engine coverage. Since it represents a large portion of the structured data on the Web, accessing Deep-Web content has been a long-standing challenge for the database community. This paper describes a system for surfacing Deep-Web content, i.e., pre-computing submissions for each HTML form and adding the resulting HTML pages into a search engine index. The results of our surfacing have been incorporated into the Google search engine and today drive more than a thousand queries per second to Deep-Web content. Surfacing the Deep Web poses several challenges. First, our goal is to index the content behind many millions of HTML forms that span many languages and hundreds of domains. This necessitates an approach that is completely automatic, highly scalable, and very efficient. Second, a large number of forms have text inputs and require valid inputs values to be submitted. We present an algorithm for selecting input values for text search inputs that accept keywords and an algorithm for identifying inputs which accept only values of a specific type. Third, HTML forms often have more than one input and hence a naive strategy of enumerating the entire Cartesian product of all possible inputs can result in a very large number of URLs being generated. We present an algorithm that efficiently navigates the search space of possible input combinations to identify only those that generate URLs suitable for inclusion into our web search index. We present an extensive experimental evaluation validating the effectiveness of our algorithms.

No abstract

Web services are loosely coupled software components, published, located, and invoked across the web. The growing number of web services available within an organization and on the Web raises a new and challenging search problem: locating desired web services. Traditional keyword search is insufficient in this context: the specific types of queries users require are not captured, the very small text fragments in web services are unsuitable for keyword search, and the underlying structure and semantics of the web services are not exploited. We describe the algorithms underlying the Woogle search engine for web services. Woogle supports similarity search for web services, such as finding similar webservice operations and finding operations that compose with a given one. We describe novel techniques to support these types of searches, and an experimental study on a collection of over 1500 web-service operations that shows the high recall and precision of our algorithms.

Abstract

This chapter studies ontology matching: the problem of finding the semantic mappings between two given ontologies. This problem lies at the heart of numerous information processing applications. Virtually any application that involves multiple ontologies must establish semantic mappings among them, to ensure interoperability. Examples of such applications arise in myriad domains, including e-commerce, knowledge management, e-learning, information extraction, bio-informatics, web services, and tourism (see Part D of this book on ontology applications).Despite its pervasiveness, today ontology matching is still largely conducted by hand, in a labor-intensive and error-prone process. The manual matching has now become a key bottleneck in building large-scale information management systems. The advent of technologies such as the WWW, XML, and the emerging Semantic Web will further fuel information sharing applications and exacerbate the problem. Hence, the development of tools to assist in the ontology matching process has become crucial for the success of a wide variety of information management applications.In response to the above challenge, we have developed GLUE, a system that employs learning techniques to semi-automatically create semantic mappings between ontologies. We shall begin the chapter by describing a motivating example: ontology matching on the Semantic Web. Then we present our GLUE solution. Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings.