Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Using Data Mining For Facilitating User Contributions In The Social Semantic Web PDF full book. Access full book title Using Data Mining For Facilitating User Contributions In The Social Semantic Web.
| Author | : |
| Publisher | : |
| Total Pages | : 176 |
| Release | : 2011 |
| Genre | : Data mining |
| ISBN | : |
| Author | : Maryam Ramezani |
| Publisher | : GRIN Verlag |
| Total Pages | : 193 |
| Release | : 2011-11 |
| Genre | : Computers |
| ISBN | : 3656047383 |
Doctoral Thesis / Dissertation from the year 2011 in the subject Computer Science - Internet, New Technologies, grade: 1,0, Karlsruhe Institute of Technology (KIT), language: English, abstract: Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute to the Web content, organize and share information, and utilize the collective knowledge of others for discovering new topics, resources and new friends. While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system's adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. This thesis utilizes data mining and machine learning techniques to address these problems. In particular, we design and develop recommender systems to aid the user in contributing to the Social Semantic Web. In addition, we study intelligent techniques to combat attacks against social tagging systems. In our work, we first propose a framework that maps domain properties to recommendation technologies. This framework provides a systematic approach to find the appropriate recommendation technology for addressing a given problem in a specific domain. Second, we improve existing graph-based approaches for personalized tag recommendation in folksonomies. Third, we develop machine learning algorithms for recommendation of semantic relations to support continuous ontology development in a social semanticWeb environment. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on t
| Author | : Tope Omitola |
| Publisher | : Springer Nature |
| Total Pages | : 138 |
| Release | : 2022-06-01 |
| Genre | : Mathematics |
| ISBN | : 3031794591 |
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).
| Author | : Michele Bonazzi |
| Publisher | : Cambridge Scholars Publishing |
| Total Pages | : 220 |
| Release | : 2016-01-14 |
| Genre | : Political Science |
| ISBN | : 1443887730 |
In the next twenty years, the convergence of robotics, informatics, nano-bio-technologies, genetics, information technologies, and cognitive sciences will have a significant impact on society. This convergence will lead to a revolution in the way that science, health, energy, resources, production, consumption and environment are conceptualised. However, these technologies will also pose new and specific challenges in terms of sustainability, ethics, and even expectations of the future. Indeed, today, the word “future” is often associated with pessimism and fear, much more than it was in the past. In order to face all these technological, ethical and cultural challenges, governments, industries and societies will need a robust cognitive framework, in order to grasp the complex dimensions of the technological convergence in progress, and must rapidly develop effective strategies to face the situations that will, unavoidably, take place. This book provides, through systemic and complexity theories, some of the theoretical tools necessary to tackle the opportunities and risks of the future.
| 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 | : Jie Tang |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2015-04-30 |
| Genre | : Mathematics |
| ISBN | : 9783031794612 |
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.
| 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.
| Author | : Cruz-Cunha, Maria Manuela |
| Publisher | : IGI Global |
| Total Pages | : 1099 |
| Release | : 2009-05-31 |
| Genre | : Computers |
| ISBN | : 1605666513 |
"This book discusses the new technologies of semantic Web, transforming the way we use information and knowledge"--Provided by publisher.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : 9783898387422 |
| Author | : Markus Schatten |
| Publisher | : |
| Total Pages | : 24 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
