Web of Data
Master Linked Data standards and principles for the Semantic Web. Learn to publish, query, and integrate structured data on the Web.
Master Linked Data standards and principles for the Semantic Web. Learn to publish, query, and integrate structured data on the Web.
This course introduces Linked Data standards and principles that form the foundation of the Semantic Web. Students will learn how to publish, obtain, and use structured data directly from the Web. The course covers core technical skills needed to leverage the Web as a space for giant structured data exchange. Topics include principles of Linked Data, the RDF data model, SPARQL query language, and integration of various data formats and sources. Through a combination of videos, quizzes, and hands-on exercises, learners will gain practical skills in working with Web data. The course prepares students to design and produce new applications and services that utilize the vast amount of structured data available on the Web.
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What you'll learn
Understand and apply Linked Data principles for publishing and obtaining Web data
Master the RDF data model and its syntaxes for data representation
Use SPARQL to query and access data sources on the Web
Integrate various data formats and sources using Web standards
Publish Linked Open Data on the Web
Search for occurrences of query graphs in datasets
Skills you'll gain
This course includes:
2.5 Hours PreRecorded video
24 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to the Web of Data, focusing on Linked Data standards and principles that underpin the Semantic Web. It is structured into four modules: 1) Principles of a Web of Linked Data, covering Web architecture and Linked Data concepts; 2) The RDF Data Model, teaching the standard model for publishing and linking data on the Web; 3) SPARQL Query Language, enabling students to query and access Web data sources; 4) Integration of other Data Formats and Sources, exploring how Web standards interact with various data formats. Throughout the course, students will learn to publish, obtain, and use structured data directly from the Web, preparing them to create applications and services that leverage the vast amount of data available online.
PRINCIPLES OF A WEB OF LINKED DATA
Module 1 · 5 Hours to complete
The RDF data model
Module 2 · 4 Hours to complete
SPARQL Query Language
Module 3 · 3 Hours to complete
Integration of other data formats and sources
Module 4 · 4 Hours to complete
Fee Structure
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Instructors
Leading AI and Semantic Web Researcher with Pioneering Work in Data Integration and Privacy
Fabien earned his PhD in 2002, pioneering the combined use of distributed artificial intelligence (AI) and the semantic Web to manage diverse data sources and users within a Web architecture. As a research project leader at Carnegie Mellon University (USA), he developed an AI method to enforce privacy preferences in querying and reasoning about personal data. In 2004, he joined Inria as a researcher, focusing on models and algorithms to integrate social media with knowledge-based AI systems on the Web, supporting human interaction. By 2012, Fabien became Inria's representative at W3C and founded Wimmics, a research team of 50 members from UCA, Inria, CNRS, and I3S, dedicated to bridging social and formal semantics on the Web using AI methods. In 2014, he was promoted to Research Director (DR). Throughout his career, he has supervised 10 PhDs, chaired major international conferences such as WWW 2012 and 2018, and published extensively with 23 journal papers, 136 conference and workshop papers, 2 books, and 11 book chapters. In 2015, Fabien launched a series of bilingual MOOCs on semantic Web and linked data. In 2017, he founded the QWANT-Inria joint research laboratory and became responsible for the research convention between the Ministry of Culture and Inria. By 2018, he was appointed Vice Head of Science at Inria Sophia Antipolis – Méditerranée.
AI Expert and Associate Professor Specializing in Web Science and Knowledge Representation
Catherine Faron Zucker holds a PhD in Computer Science from the University of Paris 6 and currently serves as an Associate Professor at the University Côte d’Azur. She teaches in the Computer Science department at Polytech Nice Sophia, where she is responsible for a training program on Web Science. Catherine also serves as the vice-head of the Wimmics research team at Inria SAM and the I3S laboratory. Her research focuses on Artificial Intelligence, particularly in Knowledge Representation and Reasoning with graphs on the Web. Her work aims to assist epistemic Web communities in accessing, capitalizing on, and sharing digital resources and knowledge. As the scientific leader of multiple research projects, Catherine’s expertise spans various application fields, including e-Education, Cultural Heritage, Health, and B2B.
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4.1 course rating
85 ratings
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