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Showing 1 - 6 of 6 matches in All Departments
Parallel and Distributed Information Systems brings together in one place important contributions and up-to-date research results in this fast moving area. Parallel and Distributed Information Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Parallel and Distributed Information Systems brings together in one place important contributions and up-to-date research results in this fast moving area. Parallel and Distributed Information Systems serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Recently, we have seen a steep increase in the popularity and adoption of XML, in areas such as traditional databases, e-business, the scientific environment, and on the web. Querying XML documents and data efficiently is a challenging issue; this book approaches search on XML data by combining content-based methods from information retrieval and structure-based XML query methods and presents the following parts: applications, query languages, retrieval models, implementing intelligent XML systems, and evaluation. To appreciate the book, basic knowledge of traditional database technology, information retrieval, and XML is needed. The book is ideally suited for courses or seminars at the graduate level as well as for education of research and development professionals working on Web applications, digital libraries, database systems, and information retrieval.
This volume contains the lecture notes of the 10th Reasoning Web Summer School 2014, held in Athens, Greece, in September 2014. In 2014, the lecture program of the Reasoning Web introduces students to recent advances in big data aspects of semantic web and linked data, and the fundamentals of reasoning techniques that can be used to tackle big data applications.
Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a longstanding goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This monograph surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and curating large knowledge bases from online content, with emphasis on semi-structured web pages with lists, tables etc., and unstructured text sources. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods. The intended audience is students and researchers interested in a wide spectrum of topics: from machine knowledge and data quality to machine learning and data science as well as applications in web content mining and natural language understanding. It will also be of interest to industrial practitioners working on semantic technologies for web, social media, or enterprise content.
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