Collaborative working has been increasingly viewed as a good practice for organizations to achieve efficiency. Organizations that work well in collaboration may have access to new sources of funding, deliver new, improved, and more integrated services, make savings on shared costs, and exchange knowledge, information and expertise. Collaboration and the Semantic Web: Social Networks, Knowledge Networks and Knowledge Resources showcases cutting-edge research on the intersections of Semantic Web, collaborative work, and social media research, exploring how the resources of so-called social networking applications, which bring people together to interact and encourage sharing of personal information and ideas, can be tapped by Semantic Web techniques, making shared Web contents readable and processable for machine and intelligent applications, as well as humans. Semantic technologies have shown their potential for integrating valuable knowledge, and they are being applied to the composition of digital learning and working platforms. Integrated semantic applications, linked data, social networks, and networked digital solutions can now be used in collaborative environments and present participants with the context-aware information that they need.

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.

The main purpose of this book is to sum up the vital and highly topical research issue of knowledge representation on the Web and to discuss novel solutions by combining benefits of folksonomies and Web 2.0 approaches with ontologies and semantic technologies. The book contains an overview of knowledge representation approaches in past, present and future, introduction to ontologies, Web indexing and in first case the novel approaches of developing ontologies.

"This book concentrates on strategies that exploit emerging technologies for the knowledge effectiveness in social networks"--Provided by publisher.

"This book lays the foundations for understanding the concepts and technologies behind the Semantic Web"--Provided by publisher.

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.

Semantic web continues to be an increasingly important system for allowing end-users to share and communicate information online. Semantic Web: Ontology and Knowledge Base Enabled Tools, Services and Application focuses on the information systems discipline and the tools and techniques utilized for the emerging use of semantic web. Covering topics on semantic search, ontologies, and recommendation systems, this publication is essential for academics, practitioners, and industry professionals.