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GraphDB Fundamentals - OntoText

GraphDB Fundamentals - Module 1: Overview of RDF & RDFS

Link -> https://graphdb.ontotext.com/free/devhub/rdfs.html 

RDF Schema, more commonly known as RDFS, adds schema to the RDF. It defines a metamodel of concepts like Resource, Literal, Class, and Datatype and relationships such as subClassOf, subPropertyOf, domain, and range. RDFS provides a means for defining the classes, properties, and relationships in an RDF model and organizing these concepts and relationships into hierarchies.

RDFS specifies entailment rules or axioms for the concepts and relationships. These rules can be used to infer new triples.

GraphDB Fundamentals - Module 2: Overview of SPARQL

Link -> https://graphdb.ontotext.com/free/devhub/sparql.html

GraphDB Fundamentals - Module 3: Ontology

Link -> https://graphdb.ontotext.com/free/devhub/ontologies.html

What is in an Ontology? Concepts and properties in a domain, Relationships  between concepts, Constraints on how the relationships can be used and Individuals as members of concepts

Why develop an ontology? A common undertanting of information, explicit domain assumptions, su pport data integration for analytics, apply domain knowledge to data, support interoperation of applications, enable model-driven applications, reduce the time and cost of application developement, improve data quality (metadata and provenance)

OWL / RDF / RDFS

    Consistency checks:Are there logical inconsistencies?
    Satisfiability checks: Are there classes that cannot have instances?
    Classification: What is the type of an instance?
    Identities:  sameAs, differentFrom, equivalentClass, equivalentProperty
    Class definitions: intersection, union, complement, disjointness
    Cardinality restrictions
    Properties definitions: object and datatypes, transitive, functional, symmetric, inverse propeties, value restrictions

Inference

Link -> https://graphdb.ontotext.com/free/devhub/inference.html

Inference is the derivation of new knowledge from existing knowledge and axioms.Facts change all the time and the amount of resources it would take to manually manage updates or rerun the inferencing process would be overwhelming without this capability. Users realize improved query speed, data availability and accurate analysis.

GraphDB supports inference out of the box and provides updates to inferred facts automatically. The system can be configured via “rule-sets” – sets of axiomatic triples and entailment rules – that determine the applied semantics. We provide full standard-compliant reasoning for RDFS, OWL-Horst, OWL2-RL and OWL2-QL as well as support for custom reasoning and performance optimized rule-sets via .pie files.

Inference is important as it uncovers the full power of data modelled with RDF(S) and ontologies. GraphDB will use existing data and the rules to infer more facts and thus produce an richer data set than the one you started with. Inference is one of the main advantages over relational databases.

Programming

From the technical perspective, GraphDB is built on top of the RDF4J, a powerful Java framework for processing and handling RDF data. This includes creating, parsing, storing, inferencing and querying over such data.

Link -> https://graphdb.ontotext.com/free/devhub/programming.html

Typically, GraphDB is used as a standalone server running as a Tomcat application. Clients then connect to that server over HTTP to execute queries and modify the data.

It is also possible to run GraphDB as an embedded database and use the RDF4J API directly on it, thus bypassing the need for a separate server or even networking. This is typically used to implement testing or simpler applications.

From Disparate Data to Visualized Knowledge

Link -> https://youtu.be/jzXdt3V64fQ

OntoRefine (inspirado no OpenRefine): xml, json, csv, tsv, ... 

rdf:sameAs para correlacionar objetos iguais, por exemplo, se uma organização X está localizada no endereço Y e uma organização Z está localizada no endereço Y então X rdf:sameAs Z (inferência)

SHACL para validação, verificar a qualidade do dado de modo iterativo, W3C,

Ingestão de dados e automatização do processo de validação, ETL com GraphDB API

Federação com SPARQL: interna (entre repositórios GraphDB) e externa (com EndPoints)

Ontop Virtual Repository: RDF 2 SQL

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