Web Data Apis For Knowledge Graphs
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Web Data APIs for Knowledge Graphs
Author: Albert Meroño-Peñuela
language: en
Publisher: Springer Nature
Release Date: 2022-05-31
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. To do so, it focuses on the paradigmatic cases of two middleware software packages, grlc and SPARQL Transformer, which automatically build and run SPARQL-based REST APIs and allow the specification of JSON schema results, respectively. The authors highlight the underlying principles behind these technologies—query management, declarative languages, new levels of indirection, abstraction layers, and separation of concerns—, explain their practical usage, and describe their penetration in research projects and industry. The book, therefore, serves a double purpose: to provide a sound and technical description of tools and methods at the disposal ofpublishers and developers to quickly deploy and consume Web Data APIs on top of Knowledge Graphs; and to propose an extensible and heterogeneous Knowledge Graph access infrastructure that accommodates a growing ecosystem of querying paradigms.
Knowledge Graphs
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.
Exploiting Linked Data and Knowledge Graphs in Large Organisations
This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and “standard” data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.