Semantic Web and IoT
21
and in most cases there is a need to combine existing ontologies under the same
framework. When multiple ontologies are used under the same development, a mapping of ontologies is important to define the concept representations of the various
ontologies relating to the same domain [118]. This can be done by using specific
properties like owl:equivalentClass and rdfs:subClassOf.
There are many studies in semantic fusion. In [131] summarizes the implementation of a functional semantic fusion system for live content from the Web. In [132]
a service-oriented platform dedicated to fusion processes has been presented. The
underlying common language for services is focused on a collection of ontologies
that allow for the representation and reasoning of various objects, circumstances
and possible threats, and so on. In [133], a use case is proposed that represents the
development of a current and future consumer knowledge base, leveraging of social
and connected open data on the basis of which any company could infer useful
information as a decision-making support. Semantic technologies perform semantic aggregation, persistence, reasoning and retrieval of information, as well as the
triggering of alerts over the semantized information.
3.3.3 Validation
The following section presents two approaches for validating RDF data, the Shape
Expressions Language [134] and the Shapes Constraint Language [135]. Both share
the same goal, that is to provide a framework for validating RDF data.
ShEx is a language for describing RDF graph structures. The basic model of this
language, also known as a ShEx schema, contains all the requirements that the RDF
data graphs under investigation must fulfill in order to be considered as valid. For
example, a requirement could be the datatype of the involved subjects or the combination of subjects, predicates and objects. Based on a list of predefined requirements,
the RDF data is tested against it and a validation report is being produced consisting
of the parts of the RDF data that do not align.
Another method for validating RDF graphs is called Shapes Constraint Language.
Similarly to ShEx, in SHACL a list of pre-defined properties define the requirements
that an RDF graph should fulfill in order to be considered as valid. Those requirements
are called shapes graphs in SHACL and the data that is validated against a shape
graph are called data graphs. Given a shapes graph and a data graph the result of
the validation process is also an RDF graph that reports the conformance of the data
graph to the shapes graph.
3.3.4 Temporal Reasoning—Stream Reasoning—CEP
(Complex Event Processing)
Incorporating the time dimension aspects in both modeling and reasoning, implicitly
is granting supplementary temporal assets in objects and knowledge representation
21
and in most cases there is a need to combine existing ontologies under the same
framework. When multiple ontologies are used under the same development, a mapping of ontologies is important to define the concept representations of the various
ontologies relating to the same domain [118]. This can be done by using specific
properties like owl:equivalentClass and rdfs:subClassOf.
There are many studies in semantic fusion. In [131] summarizes the implementation of a functional semantic fusion system for live content from the Web. In [132]
a service-oriented platform dedicated to fusion processes has been presented. The
underlying common language for services is focused on a collection of ontologies
that allow for the representation and reasoning of various objects, circumstances
and possible threats, and so on. In [133], a use case is proposed that represents the
development of a current and future consumer knowledge base, leveraging of social
and connected open data on the basis of which any company could infer useful
information as a decision-making support. Semantic technologies perform semantic aggregation, persistence, reasoning and retrieval of information, as well as the
triggering of alerts over the semantized information.
3.3.3 Validation
The following section presents two approaches for validating RDF data, the Shape
Expressions Language [134] and the Shapes Constraint Language [135]. Both share
the same goal, that is to provide a framework for validating RDF data.
ShEx is a language for describing RDF graph structures. The basic model of this
language, also known as a ShEx schema, contains all the requirements that the RDF
data graphs under investigation must fulfill in order to be considered as valid. For
example, a requirement could be the datatype of the involved subjects or the combination of subjects, predicates and objects. Based on a list of predefined requirements,
the RDF data is tested against it and a validation report is being produced consisting
of the parts of the RDF data that do not align.
Another method for validating RDF graphs is called Shapes Constraint Language.
Similarly to ShEx, in SHACL a list of pre-defined properties define the requirements
that an RDF graph should fulfill in order to be considered as valid. Those requirements
are called shapes graphs in SHACL and the data that is validated against a shape
graph are called data graphs. Given a shapes graph and a data graph the result of
the validation process is also an RDF graph that reports the conformance of the data
graph to the shapes graph.
3.3.4 Temporal Reasoning—Stream Reasoning—CEP
(Complex Event Processing)
Incorporating the time dimension aspects in both modeling and reasoning, implicitly
is granting supplementary temporal assets in objects and knowledge representation