Jose Emilio Labra Gayo – författare

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.

RDF and Linked Data have broad applicability across many fields, from aircraft manufacturing to zoology. Requirements for detecting bad data differ across communities, fields, and tasks, but nearly all involve some form of data validation. 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. Using Web addresses (URIs) as identifiers for data elements enables the construction of distributed databases on a global scale. Like the Web, the Semantic Web is heralded as an information revolution, and also like the Web, it is encumbered by data quality issues. The quality of Semantic Web data is compromised by the lack of resources for data curation, for maintenance, and for developing globally applicable data models.At the enterprise scale, these problems have conventional solutions. Master data management provides an enterprise-wide vocabulary, while constraint languages capture and enforce data structures. Filling a need long recognized by Semantic Web users, shapes languages provide models and vocabularies for expressing such structural constraints.This book describes two technologies for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL), the rationales for their designs, a comparison of the two, and some example applications.

In the last decade, we have seen tremendous successes in World Wide Web and Internet technologies. Traditional development techniques had to be adapted to thisnewsituation,andatthesametimenewtechniquesemerged.Manyresearch teams began to investigate di?erent ways to adapt classic – but still e?ective – software engineering design and development techniques to the Web and Web engineering emerged as a new research ?eld that relates several subject areas, including agents, e-commerce, e-learning, human-computer interaction, mobile technologies, security, quality and testing, the Semantic Web, etc. The International Conference on Web Engineering (ICWE) continued the tradition of earlier conferences with the aim of bringing together researchers and practitioners in these areas, trying to o?er a good environment where people with di?erent backgrounds could exchange their insights in this area. Thesuccessofthisconferencegeneratedanincredibleamountofworkforthe Program Committee members and the External Reviewers, lead by the Program Committee chairs Oscar Pastor L´ opez, Luis Olsina Santos and Mart´ ?n Gonz´ alez Rodr´ ?guez. We are immensely grateful for the e?ort they put into the process of selecting the very best papers. There were 190 papers submitted, with 25 papers accepted (13%) as long papers. A large number of papers describing ongoing research were included as short papers or posters. Unfortunately, we were forced to reject many good papers. Paper selection is always a di?cult task.

In the last decade, we have seen tremendous successes in World Wide Web and Internet technologies. Traditional development techniques had to be adapted to thisnewsituation,andatthesametimenewtechniquesemerged.Manyresearch teams began to investigate di?erent ways to adapt classic – but still e?ective – software engineering design and development techniques to the Web and Web engineering emerged as a new research ?eld that relates several subject areas, including agents, e-commerce, e-learning, human-computer interaction, mobile technologies, security, quality and testing, the Semantic Web, etc. The International Conference on Web Engineering (ICWE) continued the tradition of earlier conferences with the aim of bringing together researchers and practitioners in these areas, trying to o?er a good environment where people with di?erent backgrounds could exchange their insights in this area. Thesuccessofthisconferencegeneratedanincredibleamountofworkforthe Program Committee members and the External Reviewers, lead by the Program Committee chairs Oscar Pastor L´ opez, Luis Olsina Santos and Mart´ ?n Gonz´ alez Rodr´ ?guez. We are immensely grateful for the e?ort they put into the process of selecting the very best papers. There were 190 papers submitted, with 25 papers accepted (13%) as long papers. A large number of papers describing ongoing research were included as short papers or posters. Unfortunately, we were forced to reject many good papers. Paper selection is always a di?cult task.