Web Mining
Author | : Bettina Berendt |
Publisher | : |
Total Pages | : 218 |
Release | : 2014-01-15 |
Genre | : |
ISBN | : 9783662185919 |
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Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Web Mining From Web To Semantic Web PDF full book. Access full book title Web Mining From Web To Semantic Web.
Author | : Bettina Berendt |
Publisher | : |
Total Pages | : 218 |
Release | : 2014-01-15 |
Genre | : |
ISBN | : 9783662185919 |
Author | : Tope Omitola |
Publisher | : Morgan & Claypool Publishers |
Total Pages | : 156 |
Release | : 2015-01-01 |
Genre | : Computers |
ISBN | : 1627053999 |
The past ten years have seen a rapid growth in the numbers of people signing up to use Web-based social networks (hundreds of millions of new members are now joining the main services each year) with a large amount of content being shared on these networks (tens of billions of content items are shared each month). With this growth in usage and data being generated, there are many opportunities to discover the knowledge that is often inherent but somewhat hidden in these networks. Web mining techniques are being used to derive this hidden knowledge. In addition, the Semantic Web, including the Linked Data initiative to connect previously disconnected datasets, is making it possible to connect data from across various social spaces through common representations and agreed upon terms for people, content items, etc. In this book, we detail some current research being carried out to semantically represent the implicit and explicit structures on the Social Web, along with the techniques being used to elicit relevant knowledge from these structures, and we present the mechanisms that can be used to intelligently mesh these semantic representations with intelligent knowledge discovery processes. We begin this book with an overview of the origins of the Web, and then show how web intelligence can be derived from a combination of web and Social Web mining. We give an overview of the Social and Semantic Webs, followed by a description of the combined Social Semantic Web (along with some of the possibilities it affords), and the various semantic representation formats for the data created in social networks and on social media sites. Provenance and provenance mining is an important aspect here, especially when data is combined from multiple services. We will expand on the subject of provenance and especially its importance in relation to social data. We will describe extensions to social semantic vocabularies specifically designed for community mining purposes (SIOCM). In the last three chapters, we describe how the combination of web intelligence and social semantic data can be used to derive knowledge from the Social Web, starting at the community level (macro), and then moving through group mining (meso) to user profile mining (micro). Table of Contents: Acknowledgments / Grant Aid / Introduction and the Web / Web Mining / The Social Web / The Semantic Web / The Social Semantic Web / Social Semantic Web Mining / Social Semantic Web Mining of Communities / Social Semantic Web Mining of Groups / Social Semantic Web Mining of Users / Conclusions / Bibliography / Authors' Biographies
Author | : P. Ristoski |
Publisher | : IOS Press |
Total Pages | : 246 |
Release | : 2019-06-28 |
Genre | : Computers |
ISBN | : 1614999813 |
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.
Author | : Bettina Berendt |
Publisher | : Springer |
Total Pages | : 210 |
Release | : 2011-04-05 |
Genre | : Computers |
ISBN | : 3540301232 |
In the last years, research on Web mining has reached maturity and has broadened in scope. Two different but interrelated research threads have emerged, based on the dual nature of the Web: – The Web is a practically in?nite collection of documents: The acquisition and - ploitation of information from these documents asks for intelligent techniques for information categorization, extraction and search, as well as for adaptivity to the interests and background of the organization or person that looks for information. – The Web is a venue for doing business electronically: It is a venue for interaction, information acquisition and service exploitation used by public authorities, n- governmental organizations, communities of interest and private persons. When observed as a venue for the achievement of business goals, a Web presence should be aligned to the objectives of its owner and the requirements of its users. This raises the demand for understandingWeb usage, combining it with other sources of knowledge inside an organization, and deriving lines of action. ThebirthoftheSemanticWebatthebeginningofthedecadeledtoacoercionofthetwo threadsintwoaspects:(i)theextractionofsemanticsfromtheWebtobuildtheSemantic Web;and(ii)theexploitationofthesesemanticstobettersupportinformationacquisition and to enhance the interaction for business and non-business purposes. Semantic Web mining encompasses both aspects from the viewpoint of knowledge discovery.
Author | : Dongyan Zhao |
Publisher | : Springer |
Total Pages | : 260 |
Release | : 2014-11-17 |
Genre | : Computers |
ISBN | : 3662454955 |
This book constitutes the thoroughly refereed papers of the 8th Chinese Conference on The Semantic Web and Web Science, CSWS 2014, held in Wuhan, China, in August 2014. The 22 research papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections such as ontology reasoning and learning; semantic data generation and management; and semantic technology and applications.
Author | : Ordóñez de Pablos, Patricia |
Publisher | : IGI Global |
Total Pages | : 434 |
Release | : 2012-11-30 |
Genre | : Computers |
ISBN | : 1466624957 |
"This book provides an analysis and introduction on the concept of combining the areas of semantic web and web mining, emphasizing semantics in technologies, reasoning, content searching and social media"--Provided by publisher.
Author | : Vladan Devedžic |
Publisher | : Springer Science & Business Media |
Total Pages | : 206 |
Release | : 2010-01-08 |
Genre | : Computers |
ISBN | : 1441912193 |
According to the W3C Semantic Web Activity [1]: The Semantic Web provides a common framework that allows data to be shared and reused across appli- tion, enterprise, and community boundaries. This statement clearly explains that the Semantic Web is about data sharing. Currently, the Web uses hyperlinks to connect Web pages. The Semantic Web goes beyond that and focuses on data and envisions the creation of the web of data. On the Semantic Web, anyone can say anything about any resource on the Web. This is fully based on the concept of semantic - notations, where each resource on the Web can have an assigned meaning. This is done through the use of ontologies as a formal and explicit representation of domain concepts and their relationships [2]. Ontologies are formally based on description logics. This enables agents and applications to reason over the data when searching the Web, which has not previously been possible. Web 2. 0 has gradually evolved from letting the Web users play a more active role. Unlike the initial version of the Web, where the users mainly “consumed” content, users are now offered easy-to-use services for content production and publication. Mashups, blogs, wikis, feeds, interface remixes, and social networking/tagging s- tems are examples of these well-known services. The success and wide adoption of Web 2. 0 was in its reliance on social interactions as an inevitable characteristic of the use and life of the Web. In particular, Web 2.
Author | : Guandong Xu |
Publisher | : Springer Science & Business Media |
Total Pages | : 218 |
Release | : 2010-10-20 |
Genre | : Computers |
ISBN | : 144197735X |
This book examines the techniques and applications involved in the Web Mining, Web Personalization and Recommendation and Web Community Analysis domains, including a detailed presentation of the principles, developed algorithms, and systems of the research in these areas. The applications of web mining, and the issue of how to incorporate web mining into web personalization and recommendation systems are also reviewed. Additionally, the volume explores web community mining and analysis to find the structural, organizational and temporal developments of web communities and reveal the societal sense of individuals or communities. The volume will benefit both academic and industry communities interested in the techniques and applications of web search, web data management, web mining and web knowledge discovery, as well as web community and social network analysis.
Author | : David C. Wyld |
Publisher | : Springer Science & Business Media |
Total Pages | : 677 |
Release | : 2011-06-30 |
Genre | : Computers |
ISBN | : 364222539X |
This book constitutes the proceedings of the 4th International Conference on Network Security and Applications held in Chennai, India, in July 2011. The 63 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers address all technical and practical aspects of security and its applications for wired and wireless networks and are organized in topical sections on network security and applications, ad hoc, sensor and ubiquitous computing, as well as peer-to-peer networks and trust management.
Author | : A. Ławrynowicz |
Publisher | : IOS Press |
Total Pages | : 210 |
Release | : 2017-04-18 |
Genre | : Computers |
ISBN | : 1614997462 |
Ontologies are now increasingly used to integrate, and organize data and knowledge, particularly in data and knowledge-intensive applications in both research and industry. The book is devoted to semantic data mining – a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies and knowledge graphs, rather than only purely empirical data. The introductory chapters of the book provide theoretical foundations of both data mining and ontology representation. Taking a unified perspective, the book then covers several methods for semantic data mining, addressing tasks such as pattern mining, classification and similarity-based approaches. It attempts to provide state-of-the-art answers to specific challenges and peculiarities of data mining with use of ontologies, in particular: How to deal with incompleteness of knowledge and the so-called Open World Assumption? What is a truly “semantic” similarity measure? The book contains several chapters with examples of applications of semantic data mining. The examples start from a scenario with moderate use of lightweight ontologies for knowledge graph enrichment and end with a full-fledged scenario of an intelligent knowledge discovery assistant using complex domain ontologies for meta-mining, i.e., an ontology-based meta-learning approach to full data mining processes. The book is intended for researchers in the fields of semantic technologies, knowledge engineering, data science, and data mining, and developers of knowledge-based systems and applications.