Mean what you say: say what you mean!

Semantics is the logic of meaning and how referential relationships inform that. Concepts using natural language are the basis of the semantic model, an ontology which sets out this logic and ensures consistency of meaning.

Semantic tags reflect imaginative human concepts & vocabulary, not the linear machine terminology of applications. They should reflect how the user thinks of & might search for such content – exactly as we do on the web. Ontological hierarchies should reflect this, rather than, for instance, a KPI (which would be an attribute, not an entity). An ontology is vital for reconciling human imagination and leaps of logic with the multitude of competing & rigid models found in the typical corporate infrastructure.

You may need more than one ontology – for different user audiences (or as in some cases, different countries, different cultures, using different languages & thinking differently about the content), In this scenario, entities in common (nodes) become mutual touchpoints, allowing attributes to be localised, keeping the ontology as lean as possible by excluding irrelevant items.

The meaning of content can be encoded in terms that people and machines understand – semantic tags. This enables people to effectively harness the power of machines – speed, automation etc. to explore ideas or execute processes: to augment human capabilities.

Developing an ontology, a native semantic model is valuable because it will:

enable ontology alignment i.e. potential semantic integrity across domain or ecosystem;
curate existing content (source, lineage and context);
position content logically & consistently within a general information architecture context;
identify specific content by tagging it with a unique identifier, without which analytics become challenging or even meaningless;
identify logical relationships between concepts & entities, enriched by lineage, context and a unique identifier (URI);
programmatically describe entities, their types & their characteristics which are then able to be represented in the graph and as a sub schemas;
resolve multiple identifiers into a single concept, enabling logical errors to be detected before they enter a system.

This is what makes an ontology the ‘organising mind’, and enables the knowledge graph to apply automated reasoning which humans can then exploit to derive new knowledge which would otherwise be beyond the range of human capabilities. That is the pursuit of knowledge.