The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments

of hyperlinked documents. The essence of our approach is to augment a previous connectivity analysis based algorithm with content analysis. We identify three problems with the existing approach and devise algorithms to tackle them. The results of a user evaluation are reported that show an improvement

a surge of research activity in the area of web mining using link analysis algorithms. Subsequent to the first generation of algorithms, a significant amount of improvements and variations appeared. However, the issue of stability has received little attention in spite of its practical and theoretical implications. For instance, the issue of “link spamming ” is closely related to stability: is it possible to boost up the rank of a page by adding/removing few nodes to/from it? In this paper, we study the stability aspect of various link analysis algorithms concluding that some algorithms are more robust than others. Also, we show that those unstable algorithms may become stable when they are properly “randomized”.

This article addresses a problem with the existing approach by Clever Algorithm on studies of Web hyperlink structure: It does not take into consideration of user behavior when following multiple consecutive hyperlinks. We present an improved algorithm and show that any connected hyperlink graph

A typical learning method using the Internet is exploratory learning, where learners construct knowledge through exploring the web autonomously.

Because diverse users each modify the link structure of the WWW within a relatively small scope (by creating web-pages on their own sites that link to other web-pages), the global stucture of the WWW is unpredictable and must be analyzed a posteriori. To make effective use of the wealth of information that the WWW provides, we need a way of extracting meaningful information with respect to a global search query that we make. We focus on broad-topic queries, i.e., “Tell me about automobile manufacturers. ” A naive search strategy might be to return any page containing the phrase “automobile manufacturers”. Unfortunately, there are literally hundreds of thousands of webpages meeting this criteria, far too many for a person to look through alone. Furthermore, the websites of major automobile manufacturers are unlikely to even contain the phrase “automobile manufacturers”. So simply returning the pages with the most occurrences of the query string will obviously not suffice (we call these pages the “text-based results”). Moreover, we maintain that any real measure of the quality of a search result is inherently subjective, as it and has much to do with the context and desires of the individual making the query. We want to identify the authoritative, informative web pages for a particular query. In the above example, the Toyota home page and important reviews might be considered authorities,

AspectJ-TM is a simple and practical aspect-oriented extension to Java-TM. With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program

Information retrieval (IR) has changed considerably in the last years with the expansion of the Web (World Wide Web) and the advent of modern and inexpensive graphical user interfaces and mass storage devices. As a result, traditional IR textbooks have become quite out-of-date which has led to the introduction of new IR books recently. Nevertheless, we believe that there is still great need of a book that approaches the field in a rigorous and complete way from a computer-science perspective (in opposition to a user-centered perspective). This book is an effort to partially fulfill this gap and should be useful for a first course on information retrieval as well as for a graduate course on the topic. The book

The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions- the Web of Data. In this article we present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. We describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

Currently computers are changing from single isolated devices to entry points into a world wide network of information exchange and business transactions called the World Wide Web (WWW). Therefore support in the exchange of data, information, and knowledge exchange is becoming the key issue in current computer technology. Ontologies provide a shared and common understanding of a domain that can be communicated between people and application systems. Therefore, they may play a major role in supporting information exchange processes in various areas. This book discusses the role ontologies will play in knowledge management and in electronic commerce. In addition, I show how arising web standards such as RDF and XML can be used as