Axel-Cyrille Ngonga Ngomo – författare

This book constitutes the refereed proceedings of the 17th International Semantic Web Conference, ESWC 2020, held in Heraklion, Crete, Greece.*The 39 revised full papers presented were carefully reviewed and selected from 166 submissions. The papers were submitted to three tracks: the research track, the resource track and the in-use track. These tracks showcase research and development activities, services and applications, and innovative research outcomes making their way into industry. The research track caters for both long standing and emerging research topics in the form of the following subtracks: ontologies and reasoning; natural language processing and information retrieval; semantic data management and data infrastructures; social and human aspects of the Semantic Web; machine learning; distribution and decentralization; science of science; security, privacy, licensing and trust; knowledge graphs; and integration, services and APIs.*The conference was heldvirtually due to the COVID-19 pandemic.Chapter ‘Piveau: A Large-scale Oopen Data Management Platform based on Semantic Web Technologies’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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.

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.

This book constitutes the refereed proceedings of the 7th International Conference on Knowledge Engineering and the Semantic Web, KESW 2016, held in Prague, Czech Republic, in September 2016.The 17 revised full papers presented together with 9 short papers were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on ontologies; information and knowledge extraction; data management; applications.