Web Content Mining: Tool, Technique & Concept

2014

Web Mining is extracting information from the web resources and finding interesting patterns that can be useful from ever expanding database of World Wide Web. One of the subfield of Web mining is Web Content Mining. The objective of this paper is four folds. Firstly, this paper introduces Web Mining, Secondly, it tries to explain the interrelationship of Web mining with various other areas, thirdly, it explains various Web Content Mining techniques and finally the paper concludes with the analysis of these various techniques.

Web is one of the most widespread platforms for information exchange today, as it is easier to publish documents. As the number of users and providers increases, the number of documents grows, searching for information becomes a difficult and time-consuming process. Web mining uses various data mining techniques to discover useful knowledge from Web hyperlinks, page content and usage log file. The mining tools are used to scan the HTML documents, images, and text, the results is provided for the search engines.It can assist search engines in providing productive results of each search in order of their relevance. In this paper, we brief introduction to the concepts related to data mining, web mining and then an overview of different Web mining tools. We conclude by presenting a comparative table of these tools based on some pertinent criteria.

World Wide Web over the years has become an huge repository of data from which extracting useful information has become one of the biggest challenges. Furthermore the data is available in the form of structured, semi-structured, and unstructured formats. Much of the information on web is either semi-structured or unstructured. Extracting potential useful knowledge from these various formats has become a big area of research in the recent times. Web Content mining is a subset of Web mining which focuses on extracting useful patterns from the contents available in the web documents. This paper focuses on the various content mining techniques to be applied on the web documents.

Web is a platforms for information exchange, as it is simple and easy to publish documents. Searching for information becomes a difficult and time-consuming process as the web grows. Web mining uses various data mining techniques to discover useful knowledge from usage log file from the web. The mining tools are used to scan the HTML documents, images, and text, the results is provided for the search engines.It can assist search engines in providing productive results of each search in order of their relevance. In this paper, we brief introduction to the concepts related to web mining and then an overview of different Web usage mining.

World Wide Web is a repository of massive amount of data related to various fields. It is difficult to obtain the necessary and relevant information from this vast collection. Many researchers have proposed different methods for fetching out accurate results from the web for the given user queries. In this paper we have made an attempt to consolidate the works done by different researchers in the three fields of Web Mining namely, Web Content Mining, Web Structure Mining, and Web Usage Mining. A simple study of the researches done on these three domains is provided in the paper. This study provides a basic knowledge about the recent past studies on the field of Web mining to the readers, who are interested in further development of this field.

Here we are presenting a personalization process based on Web usage mining. This paper reviews the process of discovering useful patterns from the web server log file. In this a host of Web usage mining activities required for this process, including the pre-processing and integration of data from multiple sources, and common pattern discovery techniques that are applied to the integrated usage data.

Journal of Intelligent Information Systems, 2000

Research in Web mining is moving the World Wide Web toward a more useful environment in which users can quickly and easily find the information they need. Web mining refers to the discovery and analysis of data, documents, and multimedia from the World Wide Web. It includes hyperlink structure, statistical usage, and document content mining. Structure mining is concerned with the discovery of information through the analysis of Web page in and out links. This kind of information can establish the authority of a Web page, and help in page categorization. Usage mining applies data mining techniques to discover patterns in Web logs. This is useful in defining collaboration between users and refining user personal preferences. Content mining extracts concepts from the content of Web pages. Information retrieval techniques are applied to unstructured (text), semi-structured (HTML, XML), and structured (databases) Web pages to extract semantic meaning. This journal issue presents current research in Web content mining of unstructured and semi-structured Web pages. Search engines have the responsibility for extracting semantic meaning from the content of Web pages. So much information is now available that a searcher must depend upon search engines for possible information sources. With Web content as diverse as the authors creating Web pages, the search engine must understand the content of the individual Web pages for a searcher to effectively find information. This is not a trivial task. Authors of unstructured and semi-structured text may not be concerned with the automatic extraction of meaning. Typically text is written for a human audience, which is naturally capable of extracting meaning. To extract semantic meaning requires an understanding of the elements of the Web page and an understanding of the relationships between those elements. The extracted meaning must then be placed in a structure that is easily searchable in response to a query. Basically search engines consist of 3 parts-the user-interface, the spider, and the index. The user-interface is where the searcher enters keywords as a search query. These keywords represent the searcher's information need. Prior to the query, the spider had found pages on the Web. These pages are indexed as keywords, locations, and other descriptive information. The keywords selected represent the concepts expressed in the page. The search is simply a query of the index. The keywords in the query are matched to the keywords in the index, and the more matching keywords the better the page is as an information source. The location (and other information in the index) of the pages with the best matches are returned to the searcher.

Science and Education Publishing

With the advent of the World Wide Web and the emergence of e-commerce applications and social networks, organizations across the Web generate a large amount of data day-by-day. The abundant unstructured or semi-structured information on the Web leads a great challenge for both the users, who are seeking for effectively valuable information and for the business people, who needs to provide personalized service to the individual consumers, buried in the billions of web pages. To overcome these problems, data mining techniques must be applied on the Web. In this article, an attempt has been made to review the various web mining techniques to discover fruitful patterns from the Web, in detail. New concepts are also included in broad-sense for Optimal Web Mining. This paper also discusses the state of the art and survey on Web Mining that is used in knowledge discovery over the Web.

World Wide Web is a huge repository of web pages and links. It provides profusion of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. User's accesses are recorded in web logs. Because of the incredible usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web log Mining applies mining techniques in log data to extract the behaviour of users. Web log mining consists of three phases pre-processing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and pre-processing is an important process before web log mining. For discovering patterns sessions are to be constructed efficiently. This paper presents the existing work done to extracting patterns by using decision tree methodology in the technique of web log mining.