Social Semantic Web Mining
Published on 22. January 2015
Book
Paperback/Softback
XV, 138 pages
978-3-031-79458-2 (ISBN)
Description
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 extensionsto 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).
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
XV, 138 p.
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 9 mm
Weight
305 gr
ISBN-13
978-3-031-79458-2 (9783031794582)
DOI
10.1007/978-3-031-79459-9
Schweitzer Classification
Other editions
Additional editions
Tope Omitola | Sebastián A. Ríos | John G. Breslin
Social Semantic Web Mining
E-Book
06/2022
Springer
€53.49
Available for download
Persons
Tope Omitola is a Senior Research Fellow with the Web and Internet Science Group at the School of Electronics and Com puter Science in the University of Southampton. His research interests include provenance for Linked Data, semantic social data mining and text extraction, semantic search and informa tion discovery, and semantic dataset discovery and ranking. He was educated at King's College London and at Jesus College Cambridge. He has worked in a variety of programming and software engineering positions with companies such as ARM and BT Labs. He co-authored the paper "Put In Your Postcode, Out Comes the Data: A Case Study" which won the best paper award at ESWC 2010, and has published at various high-profile conferences. Dr. Omitola is a member of AAAS, ACM and IEEE.
Sebastián A. Ríos is an Assistant Professor in the Industrial Engineering Department of the University of Chile, where he is also Director of the Masters in Business Engineering. He is the Director of the CEINE Business Intelligence (BI) Research Center at the University of Chile, a collaborative applied re search effort with Telefonica Chile. His interests include BI, the Semantic Web, social networks, latent semantics, process mining and business process redesign, and he has received fund ing from CONICYT and CORFO. He completed his Ph.D. in Knowledge Engineering at the University of Tokyo in 2007 after receiving the Mobukagakusho Scholarship from the Japanese Government, and has a degree in industrial engineering and IT/- data mining from the University of Chile. Dr. Ríos was an overall winner in the 1st International Competition on Plagiarism Detection at PAN 2011, and won the best invited session award at KES 2009
John G. Breslin is a Senior Lecturer in Electronic Engineer ing at NUI Galway. He is also a Research Leader at the Insight Centre for Data Analytics at NUI Galway (formerly DERI). He has been PI/budget holder for funding totaling e1.75M, was co-PI on the e15M Líon 2 DERICSET, and led the Eu rapp study of the EU app economy for the EC. He created the SIOC framework, implemented in hundreds of applications (by Yahoo, Boeing, Vodafone, etc.) on over 25,000 websites. He has written 150 peer-reviewed publications and co-authored the book e Social Semantic Web. He is co-founder of boards.ie, adverts.ie, and StreamGlider, and is an advisor to various tech startups. Dr. Breslin has won various best paper awards (ICE GOV, ESWC, PELS) and two IIA Net Visionary Awards. He is Vice Chair of IFIP Working Group 12.7 on Social Networking Semantics and Collective Intelligence
Content
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 .