Linking
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Linking Enterprise Data
Author: David Wood
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-11-10
Enterprise data is growing at a much faster rate than traditional technologies allow. New enterprise architectures combining existing technologies are desperately needed. This book suggests a way forward by applying new techniques of the World Wide Web to enterprise information systems. Linking Enterprise Data is an edited volume contributed by worldwide leaders in Semantic Web and Linked Data research, standards development and adoption. Linking enterprise data is the application of World Wide Web architecture principles to real-world information management issues faced by commercial, not-for-profit and government enterprises. This book is divided into four sections: Benefits of applying Linked Data principles in enterprise settings, enterprise approval and support of Linked Data projects, specific Linked Data techniques and a number of real-world success stories from early enterprise adopters. Linking Enterprise Data targets professionals working as CTOs, CIOs, enterprise architects, project managers and application developers in commercial, not-for-profit and government organizations concerned with scalability, flexibility and robustness of information management systems. Computer science graduate students and researchers focusing on enterprise information integration will also benefit.
Grammar By Diagram - Second Edition
Grammar by Diagram, second edition is a book designed for anyone who wishes to improve grammatical understanding and skill. Using traditional sentence diagraming as a visual tool, the book explains how to expand simple sentences into compound, complex, and compound-complex sentences, and how to employ verbals (infinitives, gerunds, and participles) and other structures for additional variety. The text addresses the most frequent usage errors by explaining how to distinguish between adjectives and adverbs; how to avoid problems of pronoun case, agreement, and consistency; how to ensure that verbs will agree with their subjects and will be appropriate in terms of tense, aspect, voice, and mood; and how to phrase sentences to avoid errors in parallelism or placement of modifiers. Six appendices incorporate further exercises, a summary of key basics from the text, and supplemental material not included in the body of the text but useful for quick reference. This new edition includes additional exercises and has been revised and updated throughout.
Linked Lexical Knowledge Bases
Author: Iryna Gurevych
language: en
Publisher: Morgan & Claypool Publishers
Release Date: 2016-07-19
This book conveys the fundamentals of Linked Lexical Knowledge Bases (LLKB) and sheds light on their different aspects from various perspectives, focusing on their construction and use in natural language processing (NLP). It characterizes a wide range of both expert-based and collaboratively constructed lexical knowledge bases. Only basic familiarity with NLP is required and this book has been written for both students and researchers in NLP and related fields who are interested in knowledge-based approaches to language analysis and their applications. Lexical Knowledge Bases (LKBs) are indispensable in many areas of natural language processing, as they encode human knowledge of language in machine readable form, and as such, they are required as a reference when machines attempt to interpret natural language in accordance with human perception. In recent years, numerous research efforts have led to the insight that to make the best use of available knowledge, the orchestrated exploitation of different LKBs is necessary. This allows us to not only extend the range of covered words and senses, but also gives us the opportunity to obtain a richer knowledge representation when a particular meaning of a word is covered in more than one resource. Examples where such an orchestrated usage of LKBs proved beneficial include word sense disambiguation, semantic role labeling, semantic parsing, and text classification. This book presents different kinds of automatic, manual, and collaborative linkings between LKBs. A special chapter is devoted to the linking algorithms employing text-based, graph-based, and joint modeling methods. Following this, it presents a set of higher-level NLP tasks and algorithms, effectively utilizing the knowledge in LLKBs. Among them, you will find advanced methods, e.g., distant supervision, or continuous vector space models of knowledge bases (KB), that have become widely used at the time of this book's writing. Finally, multilingual applications of LLKB's, such as cross-lingual semantic relatedness and computer-aided translation are discussed, as well as tools and interfaces for exploring LLKBs, followed by conclusions and future research directions.