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In scholarly digital editing, the established practice for semantically enriching digital texts is to add markup to a linear string of characters. Graph data-models provide an alternative approach, which is increasingly being given serious consideration. Labelled-property-graph databases, and the W3c's semantic web recommendation and associated standards (RDF and OWL) are powerful and flexible solutions to many of the problems that come with embedded markup. This volume explores the combination of scholarly digital editions, the graph data-model, and the semantic web from three perspectives: infrastructures and technologies, formal models, and projects and editions.
The next major advance in the Web-Web 3.0-will be built on semantic Web technologies, which will allow data to be shared and reused across application, enterprise, and community boundaries. Written by a team of highly experienced Web developers, this book explains examines how this powerful new technology can unify and fully leverage the ever-growing data, information, and services that are available on the Internet. Helpful examples demonstrate how to use the semantic Web to solve practical, real-world problems while you take a look at the set of design principles, collaborative working groups, and technologies that form the semantic Web. The companion Web site features full code, as well as a reference section, a FAQ section, a discussion forum, and a semantic blog.
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
Managing Disaster Risks to Cultural Heritage presents case studies from different regions in the world and establishes a framework for understanding, identifying, and analysing disaster risks to immovable cultural heritage. Featuring contributions from academics and practitioners from around the globe, the book presents a comprehensive view of the scholarship relating to cultural heritage, disaster risk preparedness, and post-disaster recovery. Particular attention is given to the complex and dynamic nature of disaster risks and how they evolve during different phases of a catastrophic event, especially as hazards can create secondary effects that have greater impacts on cultural heritage, infrastructure, and economy. Arguing that risk preparedness and mitigation have historically been secondary to reactive emergency and first aid response, the book demonstrates that preparedness plans based on sound risk assessments can prevent hazards from becoming disasters. Emphasising that the protection of cultural heritage through preparedness, mitigation actions, and risk adaptation measures – especially for climate change – can contribute to the resilience of societies, the book highlights the vital role of communities in such activities. Managing Disaster Risks to Cultural Heritage will be useful to students, professionals, and scholars studying and working with cultural heritage protection. It will be of particular interest to those working in the fields of Cultural Heritage, Archaeology, Conservation and Preservation, Sustainable Development, and Disaster Studies.
Scholarly editions contextualize our cultural heritage. Traditionally, methodologies from the field of scholarly editing are applied to works of literature, e.g. in order to trace their genesis or present their varied history of transmission. What do we make of the variance in other types of cultural heritage? How can we describe, record, and reproduce it systematically? From medieval to modern times, from image to audiovisual media, the book traces discourses across different disciplines in order to develop a conceptual model for scholarly editions on a broader scale. By doing so, it also delves into the theory and philosophy of the (digital) humanities as such.
The term ‘annotation’ is associated in the Humanities and Technical Sciences with different concepts that vary in coverage, application and direction but which also have instructive parallels. This publication mirrors the increasing cooperation that has been taking place between the two disciplines within the scope of the digitalization of the Humanities. It presents the results of an international conference on the concept of annotation that took place at the University of Wuppertal in February 2019. This publication reflects on different practices and associated concepts of annotation in an interdisciplinary perspective, puts them in relation to each other and attempts to systematize their commonalities and divergences. The following dynamic visualizations allow an interactive navigation within the volume based on keywords: Wordcloud ☁ , Matrix ▦ , Edge Bundling ⊛
This work in the field of digital literary stylistics and computational literary studies is concerned with theoretical concerns of literary genre, with the design of a corpus of nineteenth-century Spanish-American novels, and with its empirical analysis in terms of subgenres of the novel. The digital text corpus consists of 256 Argentine, Cuban, and Mexican novels from the period between 1830 and 1910. It has been created with the goal to analyze thematic subgenres and literary currents that were represented in numerous novels in the nineteenth century by means of computational text categorization methods. To categorize the texts, statistical classification and a family resemblance analysis relying on network analysis are used with the aim to examine how the subgenres, which are understood as communicative, conventional phenomena, can be captured on the stylistic, textual level of the novels that participate in them.
This book discusses in detail a series of examples drawn from scholarly projects that use the OCHRE database platform (Online Cultural and Historical Research Environment). These case studies illustrate the wide range of data that can be managed with this platform and the wide variety of problems solved by OCHRE’s item-based graph data model. The unique features and design principles of the OCHRE platform are explained and justified, helping readers to imagine how the system could be used for their own data. Data generated by studies in the humanities and social sciences is often semi-structured, fragmented, highly variable, and subject to many interpretations, making it difficult to represent adequately in a conventional database. The authors examine commonly used methods of data management in the humanities and offer a compelling argument for a different approach that takes advantage of powerful computational techniques for organizing scholarly information. This book is a challenge to scholars in the humanities and social sciences, asking them to expect more from technology as they pursue their research goals. Written jointly by a software engineer and a research scholar, each with many years of experience in applying database methods to diverse kinds of scholarly data, it shows how scholars can make the most of their existing data while going beyond the limitations of commonly used software tools to represent their objects of study in a more accurate, nuanced, and flexible way.
The book is styled on a Cookbook, containing recipes - combined with free datasets - which will turn readers into proficient OpenRefine users in the fastest possible way.This book is targeted at anyone who works on or handles a large amount of data. No prior knowledge of OpenRefine is required, as we start from the very beginning and gradually reveal more advanced features. You don't even need your own dataset, as we provide example data to try out the book's recipes.