WEB REASONING E LABORATORIO

Academic Year 2020/2021 - 2° Year - Curriculum Data Science

Learning Objectives

• WEB REASONING

  knowledge and understanding: students will acquire knowledge concerning the application of standard tools recommended by the World Wide Web Consortium (W3C) to semantically represent, reason on, and query the information present on the Web.

  Applying knowledge and understanding: students will be able to construct logic models concerning various application domains, also called web ontologies, applying the standard W3C technology together with data and information present on the Web. In addition, students will be able to use the most widespread automated reasoners to determine logic inferences regarding web ontologies already constructed and, therefore to deduce implicit information present in them.

  Making judgements: students will be able to evaluate the quality of an ontology and to choose adequate semantic web tools for knowledge representation and reasoning in various situations.

  Communication skills: students will acquire adequate communication skills and appropriateness of expression in the communication of questions concerning knowledge representation and reasoning on the Web, also in presence of non expert interlocutors.

  Learning skills: students will gain the skill of adapting knowledge learned also to new contexts and to keep up-to-date by consulting specialized sources in the ambit of the Semantic Web.

• LABORATORIO

  knowledge and understanding: students will acquire knowledge concerning the application of standard tools recommended by the World Wide Web Consortium (W3C) to semantically represent, reason on, and query the information present on the Web.

  Applying knowledge and understanding: students will be able to construct logic models concerning various application domains, also called web ontologies, applying the standard W3C technology together with data and information present on the Web. In addition, students will be able to use the most widespread automated reasoners to determine logic inferences regarding web ontologies already constructed and, therefore to deduce implicit information present in them.

  Making judgements: students will be able to evaluate the quality of an ontology and to choose adequate semantic web tools for knowledge representation and reasoning in various situations.

  Communication skills: students will acquire adequate communication skills and appropriateness of expression in the communication of questions concerning tools for knowledge representation and reasoning on the Web, also in presence of non expert interlocutors.

  Learning skills: students will gain the skill of adapting knowledge learned also to new contexts and to keep up-to-date by consulting specialized sources in the ambit of the Semantic Web.

Course Structure

• WEB REASONING

  Frontal lectures in which, in addition to the explanation of the principal notions and tools of the semantic Web, various examples and case studies will be presented with the purpose of stimulating discussions in class and facilitate the understanding of the topics.

  Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

• LABORATORIO

  Laboratory: examples and exercises concerning semantic Web topics previously explained will be developed in detail. In addition, complex case studies regarding also the research activity of the teacher will be introduced. This will allow students to prepare in a proper way for examination.

  Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Detailed Course Content

• WEB REASONING

  Introduction to the Semantic Web: motivation, examples, hints at the semantic modelling method.

  
  - Resource Description Framework (RDF): RDF triples, RDF graphs, merging of RDF graphs, N-triples, Turtle.

  
  - SPARQL Protocol and RDF Query Language: graph pattern, query SELECT and CONSTRUCT, inferences.

  
  - RDF Schema (RDFS): classes, properties, relations among classes (subClassOf), among properties (subPropertyOf), and among properties and classes (domain and range). Logic operators of combination of classes and properties. RDFS plus.

  
  - Ontology Web Language 2 (OWL 2): restrictions on properties, on classes (someValuesFrom, allValuesFrom), cardinality restrictions. Inferences in OWL 2. OWL 2 EL, OWL 2 QL, OWL 2 RL profiles and their application. Notions of contradiction and satisfiability for ontologies.

  
  - Good and bad practices of ontologies modeling. Common mistakes.

  - Examples of ontologies and exercises of modeling and deduction with Protégé.

  
  - Introduction to classical logic. Description logics: motivation and basic notions. Logics AL, EL, FL, ALC, ALCN. The logic behind OWL 2: SROIQ(D). Reasoners Pellet e Hermit.

  
  - Datalog. The Semantic Rule Web Language (SWRL).

• LABORATORIO

  Exercises with Protégé environment concerning:

  - Resource Description Framework (RDF),

  - SPARQL Protocol and RDF Query Language,

  - RDF Schema (RDFS),

  - Ontology Web Language 2 (OWL 2), \1

  - Semantic Web Rule Language (SWRL).

   

  Usage of the reasoners Hermit e Pellet.

Textbook Information

• WEB REASONING
  1. A semantic Web Primer (third edition). Grigoris Antoniou, Paul Groth, Frank van Harmelen, and Rinke Hoekstra, 2012. The MIT Press, Cambrigde, Massachusetts, London, England.

  2. Semantic Web for the Working Ontologist (Second Edition). Dean Allemang and James Hendler, 2011. Elsevier.

• LABORATORIO
  1. A semantic Web Primer (third edition). Grigoris Antoniou, Paul Groth, Frank van Harmelen, and Rinke Hoekstra, 2012. The MIT Press, Cambrigde, Massachusetts, London, England.

  2. Semantic Web for the Working Ontologist (Second Edition). Dean Allemang and James Hendler, 2011. Elsevier.