scholarly journals Recommendation on semantic web pages based on conceptual prediction model

2018 ◽  
Vol 7 (1.7) ◽  
pp. 199
Author(s):  
Blessy Jenila R ◽  
Bharathi S
Keyword(s):  
Semantic Web ◽  
Prediction Model ◽  
Web Usage Mining ◽  
Web Pages ◽  
Semantic System ◽  
Novel Technique ◽  
Web Usage ◽  
New Models ◽  
Web Use ◽  
The Web

The development of trhe web has made a major test for guiding the client to the pages in their regions.Useful knowledge disclosure from web use information and acceptable learning portrayal for successful page suggestion are urgent and testing.In this paper we propose a novel technique to effectively give a better site page proposal through semantic upgrade by coordinating the space and web use learning of a site.Two new models are proposed to the learning.Semantic system is used to the web pages and the relations between the pages.Conceptional model produces a semantic system for web use information,which is the combination of learning and web use information.Various inquires have been created to inquiry about these learning base.Based on these questions ,an arrangement of suggestion methodologies have been proposed to produce fitting site page proposals to the client.The suggestion comes about have been contrasted and the outcomes got from a progressed existing Web Usage Mining(WUM)strategy.The exploratory outcomes show that the proposed technique delivers essentialy higher execution than the WUM technique.

Semantic Web Adaptation

2011 ◽  
pp. 78-88
Author(s):  
Alexander Mikroyannidis ◽  
Babis Theodoulidis
Keyword(s):  
Semantic Web ◽  
Web Sites ◽  
Web Usage Mining ◽  
New Era ◽  
Automatic Reasoning ◽  
Web Usage ◽  
User Friendly ◽  
Unstructured Information ◽  
The Web ◽  
The Way

The rate of growth in the amount of information available in the World Wide Web has not been followed by similar advances in the way this information is organized and exploited. Web adaptation seeks to address this issue by transforming the topology of a Web site to help users in their browsing tasks. In this sense, Web usage mining techniques have been employed for years to study how the Web is used in order to make Web sites more user-friendly. The Semantic Web is an ambitious initiative aiming to transform the Web to a well-organized source of information. In particular, apart from the unstructured information of today’s Web, the Semantic Web will contain machine-processable metadata organized in ontologies. This will enhance the way we search the Web and can even allow for automatic reasoning on Web data with the use of software agents. Semantic Web adaptation brings traditional Web adaptation techniques into the new era of the Semantic Web. The idea is to enable the Semantic Web to be constantly aligned to the users’ preferences. In order to achieve this, Web usage mining and text mining methodologies are employed for the semi-automatic construction and evolution of Web ontologies. This usage-driven evolution of Web ontologies, in parallel with Web topologies evolution, can bring the Semantic Web closer to the users’ expectations.

Effectiveness of Web Usage Mining Techniques in Business Application

2017 ◽  
pp. 324-350 ◽  
Author(s):  
Ahmed El Azab ◽  
Mahmood A. Mahmood ◽  
Abd El-Aziz
Keyword(s):  
Data Mining ◽  
Academic Research ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Data ◽  
Web Data Mining ◽  
Web Usage ◽  
Business Application ◽  
Common Interests ◽  
The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods

2018 ◽  
Vol 17 (06) ◽  
pp. 1743-1776 ◽  
Author(s):  
Jozef Kapusta ◽  
Michal Munk ◽  
Martin Drlik
Keyword(s):  
Web Mining ◽  
Web Usage Mining ◽  
Point Of View ◽  
Web Pages ◽  
Web Page ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Mining Methods ◽  
The Web

The different web mining methods and techniques can help to solve some typical issues of the contemporary websites, contribute to more effective personalization, improve a website structure and reorganize its web pages. However, only several papers tried to combine web structure and web usage mining (WUM) methods with this aim. The paper researches if and how the combination of selected web structure and WUM methods can identify misplaced web pages and how they can contribute to improving the website structure. The paper analyzes the relationship between the estimated importance of the web page from the web page creator’s point of view using the web structure mining method based on PageRank and visitors’ real perception of the importance of that individual web page using the WUM method based on sequence patterns analysis, which eliminates the problem with repeated visits of the same web page during one session. The results prove that the expected probability of accesses to the individual web page correlates with the observed visit rate obtained from the log files using the WUM method. Furthermore, the website can be improved based on the consequent application of the residual analysis on the obtained results. The applicability of the proposed combination of the web structure and WUM methods is presented on two case studies from different application domains of the contemporary web. As a result, the web pages, which are underestimated or overestimated by the web page creators, are successfully identified in both cases.

Effectiveness of Web Usage Mining Techniques in Business Application

The Dark Web ◽  
2018 ◽  
pp. 227-253
Author(s):  
Ahmed El Azab ◽  
Mahmood A. Mahmood ◽  
Abd El-Aziz
Keyword(s):  
Data Mining ◽  
Academic Research ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Data ◽  
Web Data Mining ◽  
Web Usage ◽  
Business Application ◽  
Common Interests ◽  
The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation

2018 ◽  
Vol 7 (3) ◽  
pp. 39-43
Author(s):  
Satyaveer Singh ◽  
Mahendra Singh Aswal
Keyword(s):  
Web Usage Mining ◽  
Web Usage ◽  
Navigation Patterns ◽  
Product Recommendations ◽  
Taxonomical Classification ◽  
The Comparative Study ◽  
The Web ◽  
Real World Problems

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.

A Java Technology Based Distributed Software Architecture for Web Usage Mining

Web Mining ◽  
2011 ◽  
pp. 355-372
Author(s):  
Juan M. Hernansaez
Keyword(s):  
Inductive Learning ◽  
Mining Area ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Distributed Software ◽  
Web Usage ◽  
Java Technology ◽  
Commercial Company ◽  
Meta Learning ◽  
The Web

In this chapter we focus on the three approaches that seem to be the most successful ones in the Web usage mining area: clustering, association rules and sequential patterns. We will discuss some techniques from each one of these approaches, and then we will show the benefits of using METALA (a META-Learning Architecture) as an integrating tool not only for the discussed Web usage mining techniques, but also for inductive learning algorithms. As we will show, this architecture can also be used to generate new theories and models that can be useful to provide new generic applications for several supervised and non-supervised learning paradigms. As a particular example of a Web usage mining application, we will report our work for a medium-sized commercial company, and we will discuss some interesting properties and conclusions that we have obtained from our reporting.

Web Usage Mining and the Challenge of Big Data

Big Data ◽  
2016 ◽  
pp. 899-928
Author(s):  
Abubakr Gafar Abdalla ◽  
Tarig Mohamed Ahmed ◽  
Mohamed Elhassan Seliaman
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Log ◽  
Web Usage ◽  
Web Logs ◽  
Usage Patterns ◽  
Rich Data ◽  
The Web

The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.

OWL

2011 ◽  
pp. 3009-3017
Author(s):  
Adélia Gouveia ◽  
Jorge Cardoso
Keyword(s):  
World Wide Web ◽  
Semantic Web ◽  
Particle Physics ◽  
World Wide ◽  
Visual Presentation ◽  
Web Pages ◽  
Markup Language ◽  
Extensible Markup ◽  
Description Framework ◽  
The Web

The World Wide Web (WWW) emerged in 1989, developed by Tim Berners-Lee who proposed to build a system for sharing information among physicists of the CERN (Conseil Européen pour la Recherche Nucléaire), the world’s largest particle physics laboratory. Currently, the WWW is primarily composed of documents written in HTML (hyper text markup language), a language that is useful for visual presentation (Cardoso & Sheth, 2005). HTML is a set of “markup” symbols contained in a Web page intended for display on a Web browser. Most of the information on the Web is designed only for human consumption. Humans can read Web pages and understand them, but their inherent meaning is not shown in a way that allows their interpretation by computers (Cardoso & Sheth, 2006). Since the visual Web does not allow computers to understand the meaning of Web pages (Cardoso, 2007), the W3C (World Wide Web Consortium) started to work on a concept of the Semantic Web with the objective of developing approaches and solutions for data integration and interoperability purpose. The goal was to develop ways to allow computers to understand Web information. The aim of this chapter is to present the Web ontology language (OWL) which can be used to develop Semantic Web applications that understand information and data on the Web. This language was proposed by the W3C and was designed for publishing, sharing data and automating data understood by computers using ontologies. To fully comprehend OWL we need first to study its origin and the basic blocks of the language. Therefore, we will start by briefly introducing XML (extensible markup language), RDF (resource description framework), and RDF Schema (RDFS). These concepts are important since OWL is written in XML and is an extension of RDF and RDFS.

Traversal Pattern Mining in Web Usage Data

2008 ◽  
pp. 2004-2021
Author(s):  
Jenq-Foung Yao ◽  
Yongqiao Xiao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

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