Graduate School and Research Center in Digital Sciences

An Introduction to Semantic Web technologies

[WebSem]
T Technical Teaching


Abstract

The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined. It derives from W3C director Sir Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange. This course is a guided tour for a number of W3C recommendations allowing to represent (RDF/S, SKOS, OWL) and query (SPARQL) knowledge on the web as well as the underlying logical formalisms of these languages, their syntax and semantics. We will present the problems of modeling ontologies and reconciling data on the web. Finally, we will explain how to extract knowledge from textual documents using natural language processing and information extraction technologies.

Teaching and Learning Methods:Lectures and Lab sessions (group of 2 students max)

Course Policies:Attendance to Lab session is mandatory.

Bibliography

·         Grigoris Antoniou and Frank van Harmelen: A Semantic Web Primer. 2nd Edition, MIT Press, 2009. http://www.semanticwebprimer.org/

·         Dean Allemang and Jim Hendler: Semantic Web for the Working Ontologist. 1st Edition, Morgan Kaufmann, 2008. http://workingontologist.org/

·         Jeffrey T. Pollock. Semantic Web for Dummies. http://www.semanticwebfordummies.com/

·         John G. Breslin, Alexandre Passant and Stefan Decker. The Social Semantic Web, Springer Verlag, 2009. http://socialsemanticweb.net/

Requirements

Basic knowledge of web technologies (html, css, javscript) or database is a plus

Description

Learning outcomes:

·         Mastering the Semantic Web stack

o   RDF: represent knowledge on the web

o   RDFS, SKOS, OWL: build your own vocabulary

o   SPARQL: query the web of data (federated queries)

·         Information Extraction 101

o   Named Entity Recognition and Disambiguation

o   Sentiment analysis

·         Developing semantic web applications

o   The Linked Data principles: RAW data now!

o   Reconcile web data at scale using machine learning techniques

o   Interact with the Web of Data: RDFa, microdata, JSON-LD

Nb hours: 21.00

Grading Policy:  Lab 1+2+3 (40%), Final Exam (60%)

Nb hours: 21.00