Chapter 5
Applications of the Metadata Standards
5.1 Representing Scenarios
The division of a knowledge base K = (T , A) into an ontology T and a scenario A,
as introduced in Sect. 3.1, is not only formal, but also motivated by practice. Fulfilling
the role of a schema, an ontology needs to be ingested into a data infrastructure, or
implemented by it, only once; frequent updates are undesirable, since they require
a reannotation of data. For the scenarios handled by digital platforms, obversely,
data retrieval and ingest are routine operations, and so are updates, since they need
to occur whenever the represented reality changes, e.g. a new service is offered or
a new user is registered. Challenges related to I/O (or ingest and retrieval) mainly
concern the scenarios, not the ontologies, and their standardized representation by
files, streams or protocols is the main vehicle for syntactic interoperability.
Since the IRIs of resources on the semantic web can point to each other as freely
as the URLs of sites on the World Wide Web, i.e. in a graph-like way, it is natural
to visualize scenarios by graphs. These representations are referred to as knowledge
graphs. In Sect. 3.1, a scenario was defined as a tuple A = (I, A c , A r , H ) with individual names I, conceptual assertions A c , relational assertions A r and elementary
datatype property assertions H . The corresponding knowledge graph is a labelled
graph G = (I, E, Λ v , Λ e ) where the vertices are given by I and the edges by
E = {(I, J ) | ∃R ∈ R : (I, R, J ) ∈ A r } ⊆ I
2
.
(5.1)
Vertices are labelled according to the function Λ v : I → 2
C∪R∪
that maps
1 each
individual name I ∈ I to a set of labels
Λ v (I ) = (A c (I ) ∩ C) ∪ {v ∈
| ∃k ∈
: (k, v) ∈ H (I )},
(5.2)
1 Notation: 2 C∪R∪ is the power set (i.e. set of sets) over concept names for labelling individuals
by class, reals for numerical datatype properties (including Booleans with 1 for true, 0 for false, as
explicitly permitted by XSD) and words for textual datatype properties.
© The Author(s) 2021
M. Horsch et al., Data Technology in Materials Modelling,
SpringerBriefs in Applied Sciences and Technology,
https://doi.org/10.1007/978-3-030-68597-3_5
71
Applications of the Metadata Standards
5.1 Representing Scenarios
The division of a knowledge base K = (T , A) into an ontology T and a scenario A,
as introduced in Sect. 3.1, is not only formal, but also motivated by practice. Fulfilling
the role of a schema, an ontology needs to be ingested into a data infrastructure, or
implemented by it, only once; frequent updates are undesirable, since they require
a reannotation of data. For the scenarios handled by digital platforms, obversely,
data retrieval and ingest are routine operations, and so are updates, since they need
to occur whenever the represented reality changes, e.g. a new service is offered or
a new user is registered. Challenges related to I/O (or ingest and retrieval) mainly
concern the scenarios, not the ontologies, and their standardized representation by
files, streams or protocols is the main vehicle for syntactic interoperability.
Since the IRIs of resources on the semantic web can point to each other as freely
as the URLs of sites on the World Wide Web, i.e. in a graph-like way, it is natural
to visualize scenarios by graphs. These representations are referred to as knowledge
graphs. In Sect. 3.1, a scenario was defined as a tuple A = (I, A c , A r , H ) with individual names I, conceptual assertions A c , relational assertions A r and elementary
datatype property assertions H . The corresponding knowledge graph is a labelled
graph G = (I, E, Λ v , Λ e ) where the vertices are given by I and the edges by
E = {(I, J ) | ∃R ∈ R : (I, R, J ) ∈ A r } ⊆ I
2
.
(5.1)
Vertices are labelled according to the function Λ v : I → 2
C∪R∪
that maps
1 each
individual name I ∈ I to a set of labels
Λ v (I ) = (A c (I ) ∩ C) ∪ {v ∈
| ∃k ∈
: (k, v) ∈ H (I )},
(5.2)
1 Notation: 2 C∪R∪ is the power set (i.e. set of sets) over concept names for labelling individuals
by class, reals for numerical datatype properties (including Booleans with 1 for true, 0 for false, as
explicitly permitted by XSD) and words for textual datatype properties.
© The Author(s) 2021
M. Horsch et al., Data Technology in Materials Modelling,
SpringerBriefs in Applied Sciences and Technology,
https://doi.org/10.1007/978-3-030-68597-3_5
71