Knowledge Graphs Fundamentals Techniques And Applications Pdf Download !!INSTALL!!

The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

After presenting introductory and background material, the text covers techniques for constructing knowledge graphs, adding new knowledge to (or refining old knowledge in) knowledge graphs, and accessing (or querying) knowledge graphs. Finally, the book describes specific knowledge graph ecosystems, with each ecosystem corresponding to several real-world applications and case studies. Each chapter concludes with a software and resources section as well as bibliographic notes that suggest required reading. End-of-chapter exercises, 130 in all, represent various levels of abstraction.

Knowledge Graphs Fundamentals Techniques And Applications Pdf Download

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The heart of the knowledge graph is a knowledge model: a collection of interlinked descriptions of concepts, entities, relationships and events. Knowledge graphs put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing.

DBPedia. This project leverages the structure inherent in the infoboxes of Wikipedia to create an enormous dataset of 4.58 things (link ) and an ontology that has encyclopedic coverage of entities such as people, places, films, books, organizations, species, diseases, etc. This dataset is at the heart of the Open Linked Data movement. It has been invaluable for organizations to bootstrap their internal knowledge graphs with millions of crowdsourced entities.

As we have already seen, there are many freely available interlinked facts from sources such as DBpedia, GeoNames, Wikidata and so on, and their number continues to grow every day. However, the real power of knowledge graphs comes when we transform our own data into RDF triples and then connect our proprietary knowledge to open global knowledge.

Ontotext Platform implements all flavors of this interplay linking text and big knowledge graphs to enable solutions for content tagging, classification and recommendation. It is a platform for organizing enterprise knowledge into knowledge graphs, which consists of a set of databases, machine learning algorithms, APIs and tools for building various solutions for specific enterprise needs.

A number of specific uses and applications rely on knowledge graphs. Examples include data and information-heavy services such as intelligent content and package reuse, responsive and contextually aware content recommendation, knowledge graph powered drug discovery, semantic search, investment market intelligence, information discovery in regulatory documents, advanced drug safety analytics, etc.

Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph's local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.

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.

knowledge graphs, graph databases, knowledge graph embeddings, graph neural networks, ontologies, knowledge graph refinement, knowledge graph quality, knowledge bases, artificial intelligence, semantic web, machine learning

The goal of preparing this manuscript was an ambitious one, and involved drawing together and distilling down a vast amount of literature on a diverse range of topics into a set of key concepts described in an accessible way. For this reason, the manuscript has been prepared by many authors, who have lent their knowledge and expertise to the preparation of specific sections. A short version of the manuscript was first published as a tutorial paper [Hogan et al., 2021], consisting of an abridged version of the first five chapters of this book, along with a summary of how knowledge graphs are used in practice, and conclusions. However, there was not enough space to describe all of the important developments in the area. This led us to publish this book, which further includes topics relating to the creation, enrichment, quality assessment, refinement and publication of knowledge graphs, as well as formal definitions, a historical perspective, and extended discussion throughout.

The book serves as an entry point for those new to the topic, and may thus serve as a useful textbook for university courses, for researchers who are venturing into the topic for the first time, and for practitioners who wish to understand more about how knowledge graphs might be of use within their company or organisation, or indeed, how to maximise the value of the knowledge graphs that they are currently developing. Readers who are already active within specific sub-areas of Knowledge Graphs may further appreciate the technical definitions included, the references to other literature provided, and the broader perspective that this book offers in terms of the other related sub-areas and how they complement each other.

By drawing together diverse techniques from disparate areas, Knowledge Graphs has become an exciting topic in terms of both research and applications. We expect to see growing interest on this topic as the years advance, and indeed hope that this book will help to more firmly establish the foundations of this topic, and to foster future developments upon these foundations, potentially by its readers.

In summary, the decision to build and use a knowledge graph opens up a range of techniques that can be brought to bear for integrating and extracting value from diverse sources of data at large scale. The goal of this book is to motivate and give a comprehensive introduction to knowledge graphs: to describe their foundational data models and how they can be queried; to discuss representations relating to schema, identity, and context; to discuss deductive and inductive ways to make knowledge explicit; to present a variety of techniques that can be used for the creation and enrichment of graph-structured data; to describe how the quality of knowledge graphs can be discerned and how they can be refined; to discuss standards and best practices by which knowledge graphs can be published; and to provide an overview of existing knowledge graphs found in practice. Our intended audience includes researchers and practitioners who are new to knowledge graphs. As such, we do not assume that readers have specific expertise on knowledge graphs.

Knowledge graphs are often assembled from numerous sources, and as a result, can be highly diverse in terms of structure and granularity. To address this diversity, representations of schema, identity, and context often play a key role, where a schema defines a high-level structure for the knowledge graph, identity denotes which nodes in the graph (or in external sources) refer to the same real-world entity, while context may indicate a specific setting in which some unit of knowledge is held true. As aforementioned, effective methods for extraction, enrichment, quality assessment, and refinement are required for a knowledge graph to grow and improve over time. 2351a5e196