Content Engineering - Data Standards Compliance

When exchanging or migrating data between information systems, compliance with professionally proven and internationally recognized standards is crucial for maintaining the value of your product information and documentation.

Within these XML-based exchange formats, there are no limits to the future quality of the data and texts contained. Our goal is the consistency of the terminology used for content indexing and the semantic structuring of the data and texts.

We offer solutions for the integration and migration of tabular data from relational databases into the semantic data models of these XML standards. As a result, data and text migration and the creation of an ontology serve the semantic retrieval of clear instructions for specialist personnel in areas of knowledge relevant to liability.

An ontology is at the heart of the integration or migration of relational inventory data into a semantic data model. It forms the conceptual backbone for all data and texts. Ontologies enable a cross-source understanding of product information and documentation.

Traditional relational data fields are organized in table columns. These are linked to concepts in ontologies or imported for migration. These concepts then refer to all data and texts in all existing systems with the same meaning.

An ontology is very similar to a terminology database. It defines preferred and non-preferred as well as superordinate and subordinate designations of concepts. Terminology is operationalized technically as an ontology. An ontology forms the basis for an indexing language of a semantic retrieval. In contrast to a search, this retrieval guarantees a binding answer to a query.

Transforming Terminology into an Ontology (RDF, RDFS / OWL)

For best results, we recommend that you capture your corporate terminology with a specialized tool such as Trados MultiTerm before importing it into a graph database. This allows you to import multilingual terminology lists and efficiently edit term entries and field values in a batch.

You can then continuously refine your ontology definition and build your terminology to create a robust indexing language. The termbase definition is the first draft of your ontology schema and can be derived directly from a termbase definition (XDT).

Converting Unstructured to Information-Typed Content (DITA)

Our process ensures that all content is meticulously categorized and linked through the ontology's indexing language. We define content as topics, self-contained structured units of information, each with a distinct title. These topics are concise enough to focus on a single subject or answer a specific question yet comprehensive enough to stand alone and be authored as distinct units.

Leveraging DITA maps, we facilitate the hierarchical arrangement of topics, enabling single-source publishing and minimizing redundancy. This approach is especially valuable when dealing with product variants. The result is a well-structured technical documentation and knowledge base, suitable for PDFs and content delivery platforms.

Utilizing Shapes Constraint Language (SHACL)

We use the Shapes Constraint Language (SHACL) as part of our informationtagging® methodology to ensure the accuracy and consistency of content tagging. The SHACL acts as an important validator, ensuring that different types of content are tagged correctly by the indexing language based query.