This book constitutes the refereed proceedings of the 18th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2012, held in Galway City, Ireland, in October 2012. The 44 revised full papers were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections on knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, application of knowledge engineering, and demonstrations.

The Semantic Web aims at enriching the existing Web with meta-data and processing methods so as to provide web-based systems with advanced capabilities, in particular with context awareness and decision support. The objective of this book is to provide a coherent introduction to semantic web methods and research issues with a particular emphasis on reasoning.

The 7th reasoning web Summer School, held in August 2011, focused on the central topic of applications of reasoning for the emerging Web of Data . The 12 chapters in the present book provide excellent educational material as well as a number of references for further reading. The book not only addresses students working in the area, but also those seeking an entry point to various topics related to reasoning over Web data. "

This book constitutes the refereed proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, held in Passau, Germany, in June 2015. The 18 full papers, 15 short papers, 14 poster and demonstration papers presented were carefully reviewed and selected from 100 submissions. The papers cover the following topics: information extraction, distributional semantics, querying and question answering systems, context-aware NLP, cognitive and semantic computing, sentiment and opinion analysis, information extraction and social media, NLP and usability, text classification and extraction, and posters and demonstrations.

The Semantic Web is one of the major current endeavours of applied computer science. The Semantic Web aims at enriching the existing Web with meta-data and processing methods so as to provide Web-based systems with advanced (- called intelligent) capabilities, in particular with context-awarenessand decision support. TheadvancedcapabilitiesrequiredinmostSemanticWebapplicationscen- ios primarily call for reasoning. Reasoning capabilities are o?ered by Semantic Web languages that are currently being developed. Most of these languages, however,are developed mainly from functionality-centred perspectives (e.g., - tology reasoning or access validation) or application-centred perspectives (e.g., Web service retrieval and composition). A perspective centred on the reasoning techniques complementing the above-mentioned activities appears desirable for Semantic Web systems and applications. The Summer School is devoted to this perspective. The "ReasoningWeb" series of annual Summer Schools was started in 2005 on behalf of the work package "Education and Training (ET)" of the Network of Excellence REWERSE. This year's edition focused on the use of semantic technologies to enhance data access on the Web. For this reason, courses presented a range of techniques and formalisms which bridge semantic-based and data-intensive systems.

This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring:  Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification.  Its primary focus is on principles crucial for application success.   Topics and features: Focuses on approaches supported by mathematical arguments, rather than sole computing experiences Investigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from them Considers key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithms Examines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problem Addresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrization Investigates the mathematical principles involves with natural language processing and computer vision Keeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire book Although this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond� the sole computing experience.