Название: Knowledge Graphs (Synthesis Lectures on Data, Semantics, and Knowledge)
Автор: Aidan Hogan, Sebastian Neumaier, Axel-Cyrille Ngonga
Издательство: Morgan & Claypool Publishers
Год: 2022
Страниц: 258
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

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.

Much of the discussion centered on this question, and there were divergent opinions as to how knowledge graphs could (or should) be defined; how they relate to previous concepts such as graph databases, knowledge bases, ontologies, RDF graphs, property graphs, semantic networks, etc.; and how the emerging area of Knowledge Graphs should be positioned with respect to the established areas of Artificial Intelligence, Big Data, Databases, Graph Theory, Logic, Machine Learning, Knowledge Representation, Natural Language Processing, Networks (in their various forms), and the Semantic Web. As the discussion continued, a consensus began to emerge: Knowledge Graphs, as a topic, involves a novel confluence of techniques stemming from previously disparate scientific communities, with the unifying goal of developing novel graph-based techniques for better integrating and extracting value from diverse knowledge sources at large scale.

The book is divided into ten chapters. Chapter 1 provides a general introduction to the area, defines the concept of a “knowledge graph”, and provides a high-level overview of how knowledge graphs are currently being used. Chapter 2 presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried...

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.