Web Mining
#1

The purpose of Web mining is to develop methods and systems for discovering models
of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent SemanticWeb.
The World Wide Web has made an enormous amount of information electronically
accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in “e-activities” such as e-commerce, e-learning, e-government, e-science. Independently of the development of the Internet, Data Mining expanded out of the academic world into industry. Methods and their potential became known outside the academic world and commercial toolkits became available that allowed applications at an industrial scale. Numerous industrial applications have shown that models can be constructed from data for a wide variety of industrial problems. The World-Wide Web is an interesting area for Data Mining because huge amounts of information are available. Data Mining methods can be used to analyze the behavior of individual users, access patterns of pages or sites, properties of collections of documents.
Almost all standard data mining methods are designed for data that are organized as multiple “cases” that are comparable and can be viewed as instances of a single pattern, for example patients described by a fixed set of symptoms and diseases, applicants for loans, customers of a shop. A “case” is typically described by a fixed set of features (or variables). Data on the Web have a different nature. They are not so easily comparable and have the form of free text, semi-structured text (lists, tables) often with images and hyperlinks, or server logs. The aim to learn models of documents has given rise to the interest in Text Mining methods for modeling documents in terms of properties of documents. Learning from the hyperlink structure has given rise to graph-based methods, and server logs are used to learn about user behavior.

Instead of searching for a document that matches keywords, it should be possible to combine information to answer questions. Instead of retrieving a plan for a trip to Hawaii, it should be possible to automatically construct a travel plan that satisfies certain goals and uses opportunities that arise dynamically. This gives rise to a wide range of challenges. Some of them concern the infrastructure, including the interoperability of systems and the languages for the exchange of information rather than data. Many challenges are in the area of knowledge representation, discovery and engineering. They include the extraction of knowledge from data and its representation in a form understandable by arbitrary parties, the intelligent questioning and the delivery of answers to problems as opposed to conventional queries and the exploitation of formerly extracted knowledge in this process. The ambition of representing content in a way that can be understood and consumed by an arbitrary reader leads to issues in which cognitive sciences and even philosophy are involved, such as the understanding of an asset’s intended meaning.
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#2

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