WEB REASONING

According to the Dublin descriptors, students, at the end of the course, will demonstrate:

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.

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.

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).

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 (pp. 288).

2. Semantic Web for the Working Ontologist (Third Edition). Dean Allemang, James Hendler and Fabien Gandon, 2020. Morgan and Claypool (pp. 510).

FURTHER LEARNING MATERIAL: 

Main learning resources available to students are frontal classes, whose attendance, as already pointed out, are strongly recommended. 

Software project on the arguments of the course. During the discussion of the project, on the exam date, students will be asked questions concerning the motivations behind some design choices in the design of the ontology and theoretical questions concerning contructs and operators that have been used. 

The project can be also developed in small groups of two or three persons. 

In this latter case the work developed by each participant must be well documented. 

The evaluation of the exam will take into account the mastery of arguments and of acquired skills, of linguistic accuracy and of lexical appropriateness, as well as the argumentative abilities of the candidate. 

Verification of learning can be carried out also in a telematic way, in case the situation would require it.  

1) What is SPARQL and how can be used? What does a CONSTRUCT query generates?

2) What is the use of the existential restriction?

3)  What is the use of the universal restriction?

4) What is the use of the restriction of cardinality?

5) What are SWRL rules? 

Concerning the specific developed project: 

    1. How have you obtained such inference? Is it possible to obtain the same result in another way?

    2. Explain the construction of a specific hierarchy of classes.

    3. Which foundational ontologies have you used in your project?