2. Professors
  3. SENATORE Sabrina
  4. Teaching

SEMANTIC TECHNOLOGIES

Sabrina SENATORE SEMANTIC TECHNOLOGIES

0622700104
DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS
EQF7
COMPUTER ENGINEERING
2024/2025



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2022
SPRING SEMESTER
CFUHOURSACTIVITY
324LESSONS
324EXERCISES
SABRINA SENATORE T Curriculum
Objectives
LEARNING OBJECTIVES
THE COURSE PROVIDES THE METHODOLOGICAL AND TECHNOLOGICAL TOOLS AT THE BASIS OF THE SEMANTIC WEB IN ORDER TO REPRESENT, STORE, INTEGRATE, AND QUERY DATABASES, ALSO INCLUDING CLINICAL DATABASES.

KNOWLEDGE AND UNDERSTANDING
SEMANTIC WEB TECHNOLOGIES WITH PARTICULAR REFERENCE TO ONTOLOGIES AND LANGUAGES FOR KNOWLEDGE REPRESENTATION OF KNOWLEDGE (RDF/S, OWL) AND REASONING, THE PRINCIPLES OF OPERATION SEMANTIC DATA STORAGE SYSTEMS (TRIPLE STORE, GRAPH DATABASE) AND QUERY LANGUAGES (SPARQL), AND QUERY LANGUAGES (SPARQL), THE PRINCIPLES OF LINKED OPEN DATA AND REPOSITORIES AND DATABASES IN SPECIFIC DOMAIN, SUCH AS CLINIC, MEDICAL, BIOLOGICAL.

APPLIED KNOWLEDGE AND UNDERSTANDING
USE SEMANTIC TECHNOLOGIES AND PUBLIC REPOSITORIES AND KNOWLEDGE BASES TO MODEL DOMAINS OF INTEREST AND REAL-WORLD ASPECTS, INCLUDING THOSE RELATED TO MEDICAL AND OMICS DATA PRODUCED BY VARIOUS DIGITAL HEALTH APPLICATIONS. USE SEMANTIC TECHNOLOGIES TO BUILD AND QUERY KNOWLEDGE BASES, ALSO TO SUPPORT MEDICAL PERSONNEL FOR THE STUDY AND ANALYSIS OF DISEASES BY INTEGRATING THE INFORMATION AVAILABLE IN DIFFERENT PUBLIC REPOSITORIES (LINKED OPEN DATA).


MAKING JUDGMENT:
STUDENTS SHOULD BE ABLE TO:
- APPROPRIATELY MODEL AN ONTOLOGICAL SCHEMA AND POPULATE IT WITH INDIVIDUALS;
- ACCOMPLISH SUBSUMPTION-BASED MODELING REASONING
- DEFINE QUERIES IN SPARQL
- USE TRIPLE STORES FOR DATA MANIPULATION



COMMUNICATION SKILLS
STUDENTS WILL GAIN THE MASTERY OF BIG DATA AND SEMANTIC WEB TECHNOLOGY.
DURING THE COURSE, THE STUDENTS WILL STUDY A TOPIC ASSIGNED TO THEM AND THEN THEY WILL INTRODUCE IT WITH MASTERY AND CRITICAL ANALYSIS.

LEARNING SKILLS:
THE COURSE AIMS AT STIMULATING ANALYTICAL AND RESEARCH ABILITIES OF THE STUDENTS. AFTER THE COURSE, STUDENTS WILL BE ABLE TO EMPLOY THE METHODOLOGICAL AND TECHNOLOGICAL TOOLS FOR THE DESIGN OF APPLICATIONS THAT REQUIRE A SEMANTIC APPROACH.



Prerequisites
NONE
Contents
DIDACTIC UNIT 1: SEMANTIC WEB: PRELIMINARY NOTIONS.

(LECTURE/PRACTICE/LABORATORY HOURS 8/4/0)
- 1 (2 HOURS LECTURE): INTRODUCTION AND OVERVIEW; CONCEPTUAL UNDERPINNING OF THE SEMANTIC WEB; SEMANTIC WEB LAYER CAKE; BIG DATA AND LINKED DATA, KNOWLEDGE GRAPH
- 2 (2 HOURS LECTURE): METADATA AND SEMANTICS: XML, XML SCHEMA; NAMESPACE. DATA MODEL RDF; TRIPLE DEFINITION, INDIVIDUALS AND ASSERTIONS, BLANK NODES.

-3 (2 HOURS LECTURE): TURTLE: THE TRIPLE-BASED LANGUAGE; SERIALIZATION RDF/XML AND TURTLE. EXAMPLES.

- 4 (2 HOURS LECTURE): RDF-SCHEMA: CLASSES, INDIVIDUALS AND PROPERTIES; DOMAIN AND RANGE. REIFICATION. REASONING BY SUBSUMPTION

- 5 (4 HOURS PRACTICE): MODELING USING RDF VALIDATOR OF ASSERTIONS IN RDF (TRIPLES OF INDIVIDUALS) AND RDF-S (TRIPLES OF CLASSES AND PROPERTIES ASSOCIATED WITH INSTANCES AND THEIR SUBSUMPTIONS). MAPPING OF ASSERTIONS TO TURTLE.

KNOWLEDGE AND UNDERSTANDING: THE SEMANTIC WEB AND RELATED PRINCIPLES, REFERRING TO TAXONOMIES; THE RDF DATA MODEL AND THE RDF-S SCHEMA ; REASONING BY SUBSUMPTION

APPLYING KNOWLEDGE AND UNDERSTANDING: CAPABILITY TO GET A SEMANTIC MODELING OF A KNOWLEDGE/APPLICATION DOMAIN BY RDF AND RDF-S. CAPABILITY TO GENERATE NEW TRIPLES BY SUBSUMPTION. SERIALIZING RDF IN XML AND TURTLE.


DIDACTIC UNIT 2: ONTOLOGIES, SEMANTIC LANGUAGES AND SEMANTIC MODELING OF A DOMAIN.

(HOURS LECTURE/PRACTICE/LABORATORY 10/6/0)
- 6 (2 HOURS LECTURE): WHAT IS AN ONTOLOGY; DEFINITION; FORMAL APPROACH; ONTOLOGY REPRESENTATION. TAXONOMY AND ONTOLOGY

- 7 (2 HOURS LECTURE): FOUNDATIONS OF DESCRIPTION LOGIC; OWL: CLASSES AND ABSTRACT AND CONCRETE ROLES; LOGICAL OPERATORS, REASONING ON KNOWLEDGE BASE.

- 8 (2 HOURS LECTURE): ROLE RESTRICTION; OPEN WORLD ASSUMPTION (OWA). ROLE RELATIONSHIPS AND ROLE CHARACTERISTICS. OWL2: FURTHER CONSTRUCTS.



- 9 (2 HOURS LECTURE): ONTOLOGY ENGINEERING; ONTOLOGY DEVELOPMENT PROCESS; ONTOLOGY MAPPING, MATCHING ALIGNMENT

- 10 (2 HOURS LECTURE): FOAF, SKOS, SPECIFIC DOMAIN ONTOLOGIES ACCORDING TO THE DOMAIN OF INTEREST ASSIGNED IN THE HOMEWORK (E.G., GENE ONTOLOGY, SSN/SOSA, ETC.); REUSE OF ONTOLOGIES

-11 (2 HOURS PRACTICE): PROTEGÉ: ENVIRONMENT AND MODELING OF BASIC ONTOLOGY CONCEPTS. VALIDATION AND IDENTIFICATIONS OF INCONSISTENCIES (AT BOTH ABOX AND T-BOX LEVELS) OF THE DESIGNED MODEL BY USE OF THE REASONER.

-12 (4 HOURS EXERCISE): MODELING THE ONTOLOGY BY PROTEGÈ: CLASSES AND SUBSUMPTION RELATIONS, ROLE RESTRICTIONS; POPULATION. INFERENCE USING REASONER INTEGRATED INTO PROTEGÈ (E.G., PELLET) FOR ONTOLOGY VALIDATION. CONSISTENCY CHECKING OF THE DEFINED KNOWLEDGE BASE AND FIXING ANY INCONSISTENCIES.

KNOWLEDGE AND UNDERSTANDING: CONCEPT OF ONTOLOGY, ONTOLOGY SCHEMA AND POPULATION (KNOWLEDGE BASE). DISTINCTION BETWEEN A-BOX AND T-BOX. INFERENCE BY CLASS RESTRICTIONS. ONTOLOGICAL MAPPING AND ALIGNMENT. KNOWN ONTOLOGIES.

APPLYING KNOWLEDGE AND UNDERSTANDING: MODEL AN ONTOLOGY FROM A DOMAIN OF INTEREST. DEFINING THE ONTOLOGY SCHEMA, POPULATION. USE AND INTEGRATION OF KNOWN ONTOLOGIES. VERIFICATION AND CONSISTENCY OF THE MODEL.

DIDACTIC UNIT 3: THE SPARQL QUERY LANGUAGE.

(HOURS LECTURE/EXERCISE/LABORATORY 4/8/0)
- 13 (4 HOURS LECTURE): THE SPARQL LANGUAGE ; SPARL ENDPOINT; RDF-BASED QUERY LANGUAGE; NAMED GRAPHS, THE MAIN TRIPLESTORES; THE SPARQL SEVER APACHE JENA FUSEKI.

12- (2 HOURS EXERCISE): SPARQL QUERIES FROM PROTEGÉ
13- (2 HOURS EXERCISE): IMPORTING KNOWLEDGE BASE AND SPARQL QUERIES INTO APACHE JENA FUSEKI
14-(4 HOURS EXERCISE): CONSTRUCTION OF A KNOWLEDGE BASE STARTING WITH DEFINING THE ONTOLOGY SCHEMA, POPULATING THE ONTOLOGY AND SPARQL QUERIES, ASSIGNED A DOMAIN OF INTEREST. REUSE OF ONTOLOGIES STUDIED.

KNOWLEDGE AND UNDERSTANDING: THE SPARQL PROTOCOL/LANGUAGE; SPARQL QUERY FORMS FOR KNOWLEDGE BASE QUERIES.
APPLYING KNOWLEDGE AND UNDERSTANDING: DEFINING QUERIES ON THE KNOWLEDGE BASE; GENERATING NEW TRIPLES USING SPARQL QUERY CLAUSES AND EXTENDING ONTOLOGY.

DIDACTIC UNIT 4: LINKED DATA
(HOURS LECTURE/EXERCISE/LABORATORY 4/4/0)
- 15 (2 HOURS LECTURE): LINKED (OPEN) DATA. THE PRINCIPLES OF LINKED DATA; WEB OF DATA TOPOLOGY; LINKED DATA PUBLISHING PATTERNS, BEST PRACTICES TO PRODUCE LOD; LINKED DATA AND KNOWLEDGE GRAPH.


16- (2 HOURS LECTURE/EXERCISE): THE GRAPH DATABASE NEO4J, NEO4J RDF & SEMANTICS TOOLKIT- IMPORTING AND EXPORTING LINKED DATA AS RDF GRAPHS- MAPPING BETWEEN LOCAL KNOWLEDGE BASE AND LINKED DATA- CIPHER TO QUERY KNOWLEDGE.
17- (4 HOURS EXERCISE): IMPORTING RDF-BASED ASSERTIONS DEFINED IN KNOWLEDGE BASE DEVELOPMENT (MADE IN PREVIOUS EXERCISE ACTIVITIES) IN NEO4J. INTEGRATION OF THE KNOWLEDGE BASE WITH ADDITIONAL LINKED DATA CONCEPTS FROM PUBLIC SCHEMA; VISUALIZATION OF THE RDF GRAPH AND ANY VIEWS USING QUERIES.

KNOWLEDGE AND UNDERSTANDING: LINKED DATA AND ITS ROLE IN THE WEB AND IN BIG DATA AND FOR DATA MANAGEMENT AND INTEGRATION. WAYS TO TRANSFORM STRUCTURED AND UNSTRUCTURED DATA INTO LINKED OPEN DATA.

APPLIED KNOWLEDGE AND UNDERSTANDING CAPABILITIES: ABILITY TO BROWSE AND SELECT LINKED OPEN DATA USABLE ON THE WEB AND RETRIEVE DATA OF INTEREST; LINKED DATA GRAPHS, AND BUILDING LINKED DATA FROM RDF GRAPHS.


TOTAL LECTURE/PRACTICE/LABORATORY HOURS 24/24/0
Teaching Methods
LECTURES AND CLASSROOM EXERCISES. IN THE LECTURES ARE PRESENTED THE TOPICS ALONG WITH SEVERAL EXAMPLES. IN THE EXERCISES, A TASK TO BE COMPLETED IS PROPOSED, BY USING THE TECHNIQUES PRESENTED IN FRONTAL LESSONS. IT IS EXPECTED A COLLECTIVE VERIFICATION WITH CLARIFICATION OF ANY PROBLEMS ENCOUNTERED.
Verification of learning
DISCUSSION OF THE FINAL HOMEWORK AND ORAL EXAMINATION. DURING THE COURSE, HOMEWORK WILL BE ASSIGNED FOR THE VARIOUS TEACHING UNITS. THE FINAL HOMEWORK WILL SUMMARIZE THE MAIN CONTENTS OF THE COURSE AND WILL BE PRESENTED AND DISCUSSED IN THE EXAM.
IN THE ORAL EXAM
THE STUDENT HAS TO SHOW KNOWLEDGE OF ALL THE TOPICS STUDIED IN THE COURSE.
Texts
PASCAL HITZLER, MARKUS KRÖTZSCH, SEBASTIAN RUDOLPH
FOUNDATIONS OF SEMANTIC WEB TECHNOLOGIES
CHAPMAN & HALL/CRC, 2009.

TOM HEATH AND CHRISTIAN BIZER (2011) LINKED DATA: EVOLVING THE WEB INTO A GLOBAL DATA SPACE (FIRST EDITION). SYNTHESIS LECTURES ON THE SEMANTIC WEB: THEORY AND TECHNOLOGY, 1:1, 1-136. MORGAN & CLAYPOOL.

SUPPLEMENTARY DIDACTIC MATERIAL WILL BE AVAILABLE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS.
More Information
THE COURSE IS HELD IN ENGLISH
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