Social Semantic Web Mining

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).

Social Networks and the Semantic Web offers valuable information to practitioners developing social-semantic software for the Web. It provides two major case studies. The first case study shows the possibilities of tracking a research community over the Web. It reveals how social network mining from the web plays an important role for obtaining large scale, dynamic network data beyond the possibilities of survey methods. The second case study highlights the role of the social context in user-generated classifications in content, such as the tagging systems known as folksonomies.

Online social networks have already become a bridge connecting our physical daily life with the (web-based) information space. This connection produces a huge volume of data, not only about the information itself, but also about user behavior. The ubiquity of the social Web and the wealth of social data offer us unprecedented opportunities for studying the interaction patterns among users so as to understand the dynamic mechanisms underlying different networks, something that was previously difficult to explore due to the lack of available data. In this book, we present the architecture of the research for social network mining, from a microscopic point of view. We focus on investigating several key issues in social networks. Specifically, we begin with analytics of social interactions between users. The first kinds of questions we try to answer are: What are the fundamental factors that form the different categories of social ties? How have reciprocal relationships been developed from parasocial relationships? How do connected users further form groups? Another theme addressed in this book is the study of social influence. Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. Considerable research has been conducted to verify the existence of social influence in various networks. However, few literature studies address how to quantify the strength of influence between users from different aspects. In Chapter 4 and in [138], we have studied how to model and predict user behaviors. One fundamental problem is distinguishing the effects of different social factors such as social influence, homophily, and individual's characteristics. We introduce a probabilistic model to address this problem. Finally, we use an academic social network, ArnetMiner, as an example to demonstrate how we apply the introduced technologies for mining real social networks. In this system, we try to mine knowledge from both the informative (publication) network and the social (collaboration) network, and to understand the interaction mechanisms between the two networks. The system has been in operation since 2006 and has already attracted millions of users from more than 220 countries/regions.

"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.

The book will focus on exploiting state of the art research in semantic web and web science. The rapidly evolving world-wide-web has led to revolutionary changes in the whole of society. The research and development of the semantic web covers a number of global standards of the web and cutting edge technologies, such as: linked data, social semantic web, semantic web search, smart data integration, semantic web mining and web scale computing. These proceedings are from the 6th Chinese Semantics Web Symposium.

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.

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.