Semantic Enrichment - Leverage Tacit Information

Discover hidden treasures in your company data by merging and semantically enriching product data and product documentation. We merge various information silos into an informational digital twin of your products and services.

Until now, data and texts were mainly stored in relational databases. Each complex product had to be transferred into a schema of tables, columns and references. The semantics of this data were hidden in cryptically abbreviated table and column headings, in the equally cryptic documentation of entity relationship models, in the outdated program code for processing the data and texts contained therein and in the outdated minds of the developers responsible.

End of the line inventory system

However, the decision to replace or integrate existing systems is of secondary importance. A minimally invasive deployment requires the retention of a running existing system with existing and familiar user interfaces for data maintenance.

The decisive factor is your company’s ability to innovate. This depends directly on reliable access to all information resources and the possibilities of new information queries. This is only possible through the semantic expansion of existing data models.

Thinking ahead with existing data on a semantic level

An additional semantic data layer describes the contained or linked data and texts and makes them accessible to people and intelligent agents. A semantic data layer overlays the technical data layer and integrates heterogeneous data sources. It forms the basis for replacing or extending the data layer of conventional database systems.

Tabular data is a reality of the world simplified into homogeneous structures. A cross-data semantic layer enables enrichment with previously neglected or new additional information about your products and services. A new semantic layer enables the enrichment of previously hidden knowledge in the minds of employees.

The enrichment of tacit knowledge is supplemented by the possibility of supporting different knowledge views. For example, product management and marketing have different classifications and terminology. Products are grouped by these two departments either purely according to technical characteristics or according to characteristics of saleable solutions for industries.