This book redefines community discovery in the new world of Online Social Networks and Web 2.0 applications, through real-world problems and applications in the context of the Web, pointing out the current and future challenges of the field. Particular emphasis is placed on the issues of community representation, efficiency and scalability, detection of communities in hypergraphs, such as multi-mode and multi-relational networks, characterization of social media communities and online privacy aspects of online communities. User Community Discovery is for computer scientists, data scientists, social scientists and complex systems researchers, as well as students within these disciplines, while the connections to real-world problem settings and applications makes the book appealing for engineers and practitioners in the industry, in particular those interested in the highly attractive fields of data science and big data analytics.

This book redefines community discovery in the new world of Online Social Networks and Web 2.0 applications, through real-world problems and applications in the context of the Web, pointing out the current and future challenges of the field. Particular emphasis is placed on the issues of community representation, efficiency and scalability, detection of communities in hypergraphs, such as multi-mode and multi-relational networks, characterization of social media communities and online privacy aspects of online communities. User Community Discovery is for computer scientists, data scientists, social scientists and complex systems researchers, as well as students within these disciplines, while the connections to real-world problem settings and applications makes the book appealing for engineers and practitioners in the industry, in particular those interested in the highly attractive fields of data science and big data analytics.

This book presents the state of the art in the areas of ontology evolution and knowledge-driven multimedia information extraction, placing an emphasis on how the two can be combined to bridge the semantic gap. This was also the goal of the EC-sponsored BOEMIE (Bootstrapping Ontology Evolution with Multimedia Information Extraction) project, to which the authors of this book have all contributed. The book addresses researchers and practitioners in the field of computer science and more specifically in knowledge representation and management, ontology evolution, and information extraction from multimedia data. It may also constitute an excellent guide to students attending courses within a computer science study program, addressing information processing and extraction from any type of media (text, images, and video). Among other things, the book gives concrete examples of how several of the methods discussed can be applied to athletics (track and field) events.

This book constitutes the refereed proceedings of the 11th International Conference on User Modeling, UM 2007, held in Corfu, Greece in July 2007.

The 30 revised full papers and 32 poster papers presented together with papers of 5 selected doctoral consortium articles and the abstracts of 3 invited lectures were carefully reviewed and selected from 169 submissions. The papers are organized in topical sections on evaluating user/student modeling techniques, data mining and machine learning for user modeling, collaborative filtering and recommender systems, cognitive modeling, user adaptation and usability, modeling affect and meta-cognition, mobile, ubiquitous and context aware user modeling, as well as intelligent information retrieval, information filtering and content personalization.

The 7th International Colloquium on Grammatical Inference (ICGI 2004) was heldintheNationalCentreforScienti?cResearch"Demokritos,"Athens, Greece on October 11-13, 2004. ICGI 2004 was the seventh in a series of successful biennial international conferences in the area of grammaticalinference. Previous meetings were held in Essex, UK; Alicante, Spain; Montpellier, France; Ames, Iowa, USA; Lisbon, Portugal; and Amsterdam, The Netherlands. This series of conferences seeks to provide a forum for the presentation and discussion of original research papers on all aspects of grammatical inference. Grammatical inference, the study of learning grammars from data, is an - tablishedresearch?eldinarti?cialintelligence, datingbacktothe1960s, andhas been extensively addressed by researchers in automata theory, language acqui- tion, computational linguistics, machine learning, pattern recognition, compu- tional learning theory and neural networks. ICGI 2004 emphasized the multid- ciplinary natureoftheresearch?eldandthe diversedomains inwhich gramm- ical inference is being applied, such as natural language acquisition, compu- tionalbiology, structuralpatternrecognition, informationretrieval, Webmining, text processing, data compression and adaptive intelligent agents. We received 45 high-quality papers from 19 countries. The papers were - viewed by at least two - in most cases three - reviewers. In addition to the 20 full papers, 8 short papers that received positive comments from the reviewers were accepted, and they appear in a separate section of this volume. The t- ics of the accepted papers vary from theoretical results of learning algorithms to innovative applications of grammatical inference, and from learning several interesting classes of formal grammars to estimations of probabilistic grammars.

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.