1.2 Semantic Interoperability
5
what code of conduct needs to be followed? Pragmatic interoperability concerns such
requirements and recommendations pertaining to the practice of communicating and
dealing with data [33–35]. If this is to be implemented in a machine-processable way,
this is inseparable from semantic technology, and closely related techniques can be
used to specify semantic and pragmatic interoperability standards [35–37].
1.3 Semantic Assets and Metadata Categories
The purpose of semantic assets and metadata models, in particular, is the description
of a research object in all its relevant aspects. It is advisable to define categories for
this description, since the aspects might differ in their specificity. Some are general or
subject to every discipline (such as file size or authorship), whereas others only apply
to a single domain. Also, existing metadata standards and ontologies usually cover
specific aspects of a description and then may be used as building blocks. Moreover,
in big data science, automated extractability of semantic information gets crucial,
and the different aspects described are differently hard to extract. In the following,
we categorize the semantic description in four main classes. These originally stem
from computational engineering [38], but also hold for materials modelling:
1. Technical metadata describe technical characteristics of the research asset,
i.e. basically, the file attributes on a filesystem level and other syntactic information. These can not only be file sizes, checksum information, storage location
or access dates, but also file formats.
2. Descriptive metadata provide general information about the research asset, such
as the authors of the data, some keywords or a title. The data are described contentwise from a higher logical standpoint.
3. Process metadata describe the generation process and the provenance of the
research asset, for example, the computational environment and software used
to generate or process the data. This description may include several linked,
consecutive steps.
4. Domain-specific metadata describe the research objects from the domainspecific perspective. In computational engineering, this includes details about
the simulated target system, the simulation method or the spatial and temporal
resolution, for example.
These four dimensions are the core of every rich data description. The four classes
are found to hold not only for engineering but also for different fields of science. It
is now subject to the metadata engineer to fill the categories with content, and we
will learn how to do this in Chap. 2 taking the example of EngMeta.
The specificity of the categories is in ascending order (1–4), which is also shown
in Fig. 1.2. Whereas the technical and descriptive categories and their metadata keys
are generic and hold for different fields of science, category of process information
is heavily bound to the research process and the domain-specific category to the
research object. However, the content of the classes may overlap and a metadata
5
what code of conduct needs to be followed? Pragmatic interoperability concerns such
requirements and recommendations pertaining to the practice of communicating and
dealing with data [33–35]. If this is to be implemented in a machine-processable way,
this is inseparable from semantic technology, and closely related techniques can be
used to specify semantic and pragmatic interoperability standards [35–37].
1.3 Semantic Assets and Metadata Categories
The purpose of semantic assets and metadata models, in particular, is the description
of a research object in all its relevant aspects. It is advisable to define categories for
this description, since the aspects might differ in their specificity. Some are general or
subject to every discipline (such as file size or authorship), whereas others only apply
to a single domain. Also, existing metadata standards and ontologies usually cover
specific aspects of a description and then may be used as building blocks. Moreover,
in big data science, automated extractability of semantic information gets crucial,
and the different aspects described are differently hard to extract. In the following,
we categorize the semantic description in four main classes. These originally stem
from computational engineering [38], but also hold for materials modelling:
1. Technical metadata describe technical characteristics of the research asset,
i.e. basically, the file attributes on a filesystem level and other syntactic information. These can not only be file sizes, checksum information, storage location
or access dates, but also file formats.
2. Descriptive metadata provide general information about the research asset, such
as the authors of the data, some keywords or a title. The data are described contentwise from a higher logical standpoint.
3. Process metadata describe the generation process and the provenance of the
research asset, for example, the computational environment and software used
to generate or process the data. This description may include several linked,
consecutive steps.
4. Domain-specific metadata describe the research objects from the domainspecific perspective. In computational engineering, this includes details about
the simulated target system, the simulation method or the spatial and temporal
resolution, for example.
These four dimensions are the core of every rich data description. The four classes
are found to hold not only for engineering but also for different fields of science. It
is now subject to the metadata engineer to fill the categories with content, and we
will learn how to do this in Chap. 2 taking the example of EngMeta.
The specificity of the categories is in ascending order (1–4), which is also shown
in Fig. 1.2. Whereas the technical and descriptive categories and their metadata keys
are generic and hold for different fields of science, category of process information
is heavily bound to the research process and the domain-specific category to the
research object. However, the content of the classes may overlap and a metadata