Synthesis Lectures on Data, Semantics, and Knowledge – serie

This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice.\ It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people.

This book describes a set of methods, architectures, and tools to extend the data pipeline at the disposal of developers when they need to publish and consume data from Knowledge Graphs (graph-structured knowledge bases that describe the entities and relations within a domain in a semantically meaningful way) using SPARQL, Web APIs, and JSON.

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia.

The book synthesizes research on the analysis of biomedical ontologies using formal concept analysis, including through auditing, curation, and enhancement. As the evolution of biomedical ontologies almost inevitably involves manual work, formal methods are a particularly useful tool for ontological engineering and practice, particularly in uncovering unexpected "bugs" and content materials. The book first introduces simple but formalized strategies for discovering undesired and incoherent patterns in ontologies before exploring the application of formal concept analysis for semantic completeness. The book then turns to formal concept analysis, a classical approach used in the mathematical treatment of orders and lattices, as an ontological engineering principle, focusing on the structural property of ontologies with respect to its conformation to lattice or not (non-lattice). The book helpfully covers the development of more efficient algorithms for non-latticedetection and extraction required by exhaustive lattice/non-lattice analysis. The book goes on to highlight the power and utility of uncovering non-lattice structure for debugging ontologies and describes methods that leverage the linguistic information in concept names (labels) for ontological analysis. It also addresses visualization and performance evaluation issues before closing with an overview and forward-looking perspectives on the field. This book is intended for graduate students and researchers interested in biomedical ontologies and their applications. It can be a useful supplement for courses on knowledge representation and engineering and also provide readers with a reference for related scientific publications and literature to assist in identifying potential research topics. All mathematical concepts and notations used in this book can be found in standard discrete mathematics textbooks, and the appendix at the end of the book provides a list of key ontological resources, as well as annotated non-lattice and lattice examples that were discovered using the authors' methods, demonstrating how "bugs are fixed" by converting non-lattices to lattices with minimal edit changes.

The book synthesizes research on the analysis of biomedical ontologies using formal concept analysis, including through auditing, curation, and enhancement.

This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand.

This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand.

This book provides a coherent and unifying view for logic and representation learning to contribute to knowledge graph (KG) reasoning and produce better computational tools for integrating both worlds.

This book provides a coherent and unifying view for logic and representation learning to contribute to knowledge graph (KG) reasoning and produce better computational tools for integrating both worlds.  To this end, logic and deep neural network models are studied together as integrated models of computation.  This book is written for readers who are interested in KG reasoning and the new perspective of neuro-symbolic integration and have prior knowledge to neural networks and deep learning.  The authors first provide a preliminary introduction to logic and background knowledge closely related to the surveyed techniques such as the introduction of knowledge graph and ontological schema and the technical foundations of first-order logic learning.  Reasoning techniques for knowledge graph completion are presented from three perspectives, including: representation learning-based, logical, and neuro-symbolic integration.  The book then explores question answering on KGs with specific focus on multi-hop and complex-logic query answering before outlining work that addresses the rule learning problem.  The final chapters highlight foundations on ontological schema and introduce its usage in KG before closing with open research questions and a discussion on the potential directions in the future of the field.

Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data.

Table of Contents: Scholarly Networking Needs and Desires / The VIVO Ontology / Implementing VIVO and Filling It with Life / Case Study: University of Colorado at Boulder / Case Study: Weill Cornell Medical College / Extending VIVO / Analyzing and Visualizing VIVO Data / The Future of VIVO: Growing the Community

On the Semantic Web, such an infrastructure includes shared metadata models, ontologies, and logical reasoning, and is supported by shared ontology and other Web services alleviating the use of the new technology and linked data in legacy cataloging systems.

Table of Contents: Semantic Data Management: A Human-driven Process / Fundamentals of Motivation and Incentives / Case Study: Motivating Employees to Annotate Content / Case Study: Building a Community of Practice Around Web Service Management and Annotation / Case Study: Games with a Purpose for Semantic Content Creation / Conclusions

The surge of interest in the REpresentational State Transfer (REST) architectural style, the Semantic Web, and Linked Data has resulted in the development of innovative, flexible, and powerful systems that embrace one or more of these compatible technologies.

Table of Contents: Preface / Acknowledgments / Introduction / A Data Journalism Scenario / The PROV Ontology / Provenance Recipes / Validation, Compliance, Quality, Replay / Provenance Management / Conclusion / Bibliography / Authors' Biographies / Index

The dramatic progress of smartphone technologies has ushered in a new era of mobile sensing, where traditional wearable on-body sensors are being rapidly superseded by various embedded sensors in our smartphones.

The current drug development paradigm---sometimes expressed as, ``One disease, one target, one drug''---is under question, as relatively few drugs have reached the market in the last two decades. Meanwhile, the research focus of drug discovery is being placed on the study of drug action on biological systems as a whole, rather than on individual components of such systems. The vast amount of biological information about genes and proteins and their modulation by small molecules is pushing drug discovery to its next critical steps, involving the integration of chemical knowledge with these biological databases. Systematic integration of these heterogeneous datasets and the provision of algorithms to mine the integrated datasets would enable investigation of the complex mechanisms of drug action; however, traditional approaches face challenges in the representation and integration of multi-scale datasets, and in the discovery of underlying knowledge in the integrated datasets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed, data, has the potential to trigger system-level chemical-biological innovations. Chem2Bio2RDF is presented as an example of utilizing Semantic Web technologies to enable intelligent analyses for drug discovery.Table of Contents: Introduction / Data Representation and Integration Using RDF / Data Representation and Integration Using OWL / Finding Complex Biological Relationships in PubMed Articles using Bio-LDA / Integrated Semantic Approach for Systems Chemical Biology Knowledge Discovery / Semantic Link Association Prediction / Conclusions / References / Authors' Biographies

We give an overview of the Social and Semantic Webs, followed by a description of the combined Social Semantic Web (along with some of the possibilities it affords), and the various semantic representation formats for the data created in social networks and on social media sites.

Online social networks have already become a bridge connecting our physical daily life with the (web-based) information space.

This book describes OCLC's contributions to the transformation of the Internet from a web of documents to a Web of Data. By engaging with an international community of library and Web standards experts, they have published some of the most widely used RDF datasets representing library collections and librarianship.

In recent years, several knowledge bases have been built to enable large-scale knowledge sharing, but also an entity-centric Web search, mixing both structured data and text querying.

From a conceptual point of view, it highlights and discusses key questions that have animated the research community: what does it mean to be a Semantic Web system and how is it different from other types of systems, such as knowledge systems or web-based information systems?

This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications.

This book introduces data validation and describes its practical use in day-to-day data exchange.The Semantic Web offers a bold, new take on how to organize, distribute, index, and share data.

Each subsequent cycle reinforces and expands on what has been learned in prior cycles and introduces new related ideas. Part 1 is a cook's tour of ontology and OWL, giving an informal overview of what things need to be said to build an ontology, followed by a detailed look at how to say them in OWL.

Each subsequent cycle reinforces and expands on what has been learned in prior cycles and introduces new related ideas. Part 1 is a cook's tour of ontology and OWL, giving an informal overview of what things need to be said to build an ontology, followed by a detailed look at how to say them in OWL.

Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded.

Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens.This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios.The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.