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Summary

After market organizations in export oriented companies require correct user- and situation specific information and efficient information development, management and publication processes^[1]. “Full topic authoring in a semantic network” supports these needs.

As opposed to topic-based authoring and book based (narrative) authoring which are document oriented, full topic authoring in a semantic network is information oriented and uses a pre-defined structure in the form of a semantic information model. The semantic information model is an industry specific structure to wholly organize topic descriptions (full topic authoring). So in full topic authoring, the interdependent structural elements provide all of the relevant context necessary to substantially speedup and increase the quality of authorship, by guiding the author through the elements, attributes, structures and layout creation processes, compared to topic- and book based authoring.

Also compared to topic- and book based authoring, full topic authoring's rigid semantic information model reduces the authors' freedom to create their own elements, attributes, structures and layouts. In this manner, full topic authoring's structuring enforces consistent data and information while eliminating duplications and creating the foundation for efficient corporate information lifecycle management solutions.

This makes “full topic authoring in a semantic network” the dominant strategy for well-structured areas such as technical product communication (e.g. owner’s manual, repair manual). Less structured book based and topic-based authoring co-exists with full topic authoring and are still used for areas such as marketing information where a high degree of creativity and freedom are required.

Classifying information in a semantic information model as it occurs

A properly structured semantic information model's strict, hierarchical, bottom-up approach captures information (data and metadata) throughout the information creation process, as it occurs, enabling controlled linking to pre-defined modules and thereby optimizes easy retrieval of all relevant information. See "Linked data" (W3C).^[2]

When information is classified semantically, with a bottom-up approach, it is possible for machines to automatically and correctly read, interpret and process information. See also "Ontologies (Vocabularies)" of W3C.^[3] This significantly increases overall operational speed and efficiencies in the process of information management.

Characteristics of a semantic information model

A structure that guides authors and ensures data consistency and maximum information and translation reuse.
Concurrent multilingual information development (technical writing) is possible.
Future proof single source structure (no more data migration).
Programmatic situation specific evaluation of information.
High performance single source publishing: the same information can be delivered in any language and any format to any supported device.
Full integration with, and automatic information capture from, intelligent source and target systems (System integration) is possible.
Use of existing information from intelligent source systems whenever possible.

Full contextual (information) cueing

With semantic information modeling, full contextual (information) cueing becomes available. The retrievable intelligence from entered data is augmented by the semantic information model, so that all appropriate additional intelligence is automatically available and presented, when needed, without having been specifically requested (as in "inference" of the W3C).^[4]

Example for full contextual (information) cueing: A new dealer/garage enters the dealer network and needs to purchase expensive special tools. Only a small range of vehicles (e.g. due to country specifics) are handled by the dealer/garage. To minimize investments only special tools for this range of vehicles should be purchased. Semantic information modeling provides this type of information automatically.

Interfacing with other systems and formats

The high granularity of the information model enables interfaces to nearly every source and target system (System integration). Furthermore, combining a semantic information model with standards such as DITA or ASD S1000D combines the advantages of both, standard exchange formats and semantic information modeling. Information can also be easily exported from a semantic information model to these standards.

Specialization

The aim of a specialization is to provide industry and topic specific structuring of information units. Although some Content Management System (CMS) providers proclaim to use standards such as DITA as their data model, controlled data consistency inside the data structure is not possible. Specialized interdependence of the elements has to be managed by the author, who created it, and then they alone have the responsibility for maintenance and support of their specialization. On the other hand, a semantic information model provides an industry specialization out of the box. The provider of the industry specific semantic information model supports and maintains the structure. The responsibility is handed over to a system and an organization instead of being allocated to one or many different persons.

References

^ Managing motorbike documentation at BMW: http://www.multilingual.com/articleDetail.php?id=824
^ Linked data (W3C): http://www.w3.org/standards/semanticweb/data
^ Ontology (W3C): http://www.w3.org/standards/semanticweb/ontology
^ Inference (W3C): http://www.w3.org/standards/semanticweb/inference

External Links

Semantic Information Architecture: Creating Value by Understanding Data: http://www.information-management.com/issues/20031001/7438-1.html
Provider of systems that enable full topic authoring in a semantic network: STAR

[1] Managing motorbike documentation at BMW: http://www.multilingual.com/articleDetail.php?id=824

[2] Linked data (W3C): http://www.w3.org/standards/semanticweb/data

[3] Ontology (W3C): http://www.w3.org/standards/semanticweb/ontology

[4] Inference (W3C): http://www.w3.org/standards/semanticweb/inference

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