Metadata (metacontent), or more correctly, the vocabularies used to assemble metadata (metacontent) statements, is typically structured according to a standardised concept using a well defined metadata scheme, including: metadata standards and metadata models. Tools such as controlled vocabularies, taxonomies, thesauri, data dictionaries and metadata registries can be used to apply further standardisation to the metadata.
Metadata syntax
Metadata (metacontent) syntax refers to the rules created to structure the fields or elements of metadata (metacontent).[7] A single metadata scheme may be expressed in a number of different markup or programming languages, each of which requires a different syntax. For example, Dublin Core may be expressed in plain text, HTML, XML and RDF.[8]
A common example of (guide) metacontent is the bibliographic classification, the subject, the Dewey Decimal class number. There is always an implied statement in any "classification" of some object. To classify an object as, for example, Dewey class number 514 (Topology) (e.g. a book has this number on the spine) the implied statement is: "<book><subject heading><514>. This is a subject-predicate-object triple, or more importantly, a class-attribute-value triple. The first two elements of the triple (class, attribute) are pieces of some structural metadata having a defined semantic. The third element is a value, preferrably from some controlled vocabulary, some reference (master) data. The combination of the metadata and master data elements results in a statement which is a metacontent statement ie. "metacontent = metadata + master data". All these elements can be thought of as "vocabulary". Both metadata and master data are vocabularies which can be assembled into metacontent statements. There are many sources of these vocabularies, both meta and master data: UML, EDIFACT, XSD, Dewey/UDC/LoC, SKOS, ISO-25964, Pantone, Linnaean Binomial Nomenclature etc. Using controlled vocabularies for the components of metacontent statements, whether for indexing or finding, is endorsed by ISO-25964: "If both the indexer and the searcher are guided to choose the same term for the same concept, then relevant documents will be retrieved." This is particularly relevant when considering that the behemoth of the internet, Google, is simply indexing then matching text strings, there is no intelligence or "inferencing" occurring.
Hierarchical, linear and planar schemata
Metadata schemas can be hierarchical in nature where relationships exist between metadata elements and elements are nested so that parent-child relationships exist between the elements. An example of a hierarchical metadata schema is the IEEE LOM schema where metadata elements may belong to a parent metadata element. Metadata schemas can also be one dimensional, or linear, where each element is completely discrete from other elements and classified according to one dimension only. An example of a linear metadata schema is Dublin Core schema which is one dimensional. Metadata schemas are often two dimensional, or planar, where each element is completely discrete from other elements but classified according to two orthogonal dimensions.[9]
Metadata hypermapping
In all cases where the metadata schemata exceed the planar depiction, some type of hypermapping is required to enable display and view of metadata according to chosen aspect and to serve special views. Hypermapping frequently applies to layering of geographical and geological information overlays.[10]
Granularity
Granularity is a term that applies to data as well as to metadata. The degree to which metadata is structured is referred to as its granularity. Metadata with a high granularity allows for deeper structured information and enables greater levels of technical manipulation however, a lower level of granularity means that metadata can be created for considerably lower costs but will not provide as detailed information. The major impact of granularity is not only on creation and capture, but moreover on maintenance. As soon as the metadata structures get outdated, the access to the referred data will get outdated. Hence granularity shall take into account the effort to create as well as the effort to maintain.
Metadata syntax
Metadata (metacontent) syntax refers to the rules created to structure the fields or elements of metadata (metacontent).[7] A single metadata scheme may be expressed in a number of different markup or programming languages, each of which requires a different syntax. For example, Dublin Core may be expressed in plain text, HTML, XML and RDF.[8]
A common example of (guide) metacontent is the bibliographic classification, the subject, the Dewey Decimal class number. There is always an implied statement in any "classification" of some object. To classify an object as, for example, Dewey class number 514 (Topology) (e.g. a book has this number on the spine) the implied statement is: "<book><subject heading><514>. This is a subject-predicate-object triple, or more importantly, a class-attribute-value triple. The first two elements of the triple (class, attribute) are pieces of some structural metadata having a defined semantic. The third element is a value, preferrably from some controlled vocabulary, some reference (master) data. The combination of the metadata and master data elements results in a statement which is a metacontent statement ie. "metacontent = metadata + master data". All these elements can be thought of as "vocabulary". Both metadata and master data are vocabularies which can be assembled into metacontent statements. There are many sources of these vocabularies, both meta and master data: UML, EDIFACT, XSD, Dewey/UDC/LoC, SKOS, ISO-25964, Pantone, Linnaean Binomial Nomenclature etc. Using controlled vocabularies for the components of metacontent statements, whether for indexing or finding, is endorsed by ISO-25964: "If both the indexer and the searcher are guided to choose the same term for the same concept, then relevant documents will be retrieved." This is particularly relevant when considering that the behemoth of the internet, Google, is simply indexing then matching text strings, there is no intelligence or "inferencing" occurring.
Hierarchical, linear and planar schemata
Metadata schemas can be hierarchical in nature where relationships exist between metadata elements and elements are nested so that parent-child relationships exist between the elements. An example of a hierarchical metadata schema is the IEEE LOM schema where metadata elements may belong to a parent metadata element. Metadata schemas can also be one dimensional, or linear, where each element is completely discrete from other elements and classified according to one dimension only. An example of a linear metadata schema is Dublin Core schema which is one dimensional. Metadata schemas are often two dimensional, or planar, where each element is completely discrete from other elements but classified according to two orthogonal dimensions.[9]
Metadata hypermapping
In all cases where the metadata schemata exceed the planar depiction, some type of hypermapping is required to enable display and view of metadata according to chosen aspect and to serve special views. Hypermapping frequently applies to layering of geographical and geological information overlays.[10]
Granularity
Granularity is a term that applies to data as well as to metadata. The degree to which metadata is structured is referred to as its granularity. Metadata with a high granularity allows for deeper structured information and enables greater levels of technical manipulation however, a lower level of granularity means that metadata can be created for considerably lower costs but will not provide as detailed information. The major impact of granularity is not only on creation and capture, but moreover on maintenance. As soon as the metadata structures get outdated, the access to the referred data will get outdated. Hence granularity shall take into account the effort to create as well as the effort to maintain.
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