Clarifying Ontology and Taxonomy Terminology

Knowledge Organization Systems embrace a number of overlapping, related concepts and terminology. In this section we will define the key concepts and explain how they relate to one another.

Ontologies and Taxonomies are the foundation for building smart search and discovery applications. Semantic schema, unambiguous terminology, and accurately tagged metadata enable enterprises to deliver precision search, rich browse experiences, as well as content recommendations and the discovery of inferred facts and knowledge.

Graphite is Synaptica’s latest solution for developing and managing enterprise taxonomies and ontologies. Graphite combines taxonomy and ontology management into one seamlessly integrated user experience.

GraphDB is a highly scalable RDF graph database engine embedded with Graphite. Together, Graphite and GraphDB provide the essential tools to develop enterprise knowledge graphs, manage controlled vocabularies and metadata, and provide data analytics and business insights.

Single Source of Truth (SSOT) is the key to successful metadata management. Centralizing  and standardizing enterprise terminology involves: knowledge modelling; role-based collaboration; governance and workflow; reporting mechanisms; editorial tools to build, enrich, crosswalk, and review taxonomy schemes; and APIs and connectors to publish controlled vocabularies to content, metadata, and search systems.

Breakdown of Taxonomy Management

Knowledge Organization Systems (KOS) is a generic term that embraces taxonomies, thesauri, ontologies, classification schemes, name authorities, topic maps, and other structured terminologies.

Ontologies are a form of KOS comprising a Schema that defines all the class, property, and relationship types used in a KOS, plus a Taxonomy that contains all the specific named instances of concepts, classes, and individuals.

Schemas are the set of class, property, and relationship types that define the structure and the semantic model for an ontology.

Taxonomies are sets of specific concepts, classes, and individuals enumerated within an ontology. The entities in a taxonomy may be ordered within an hierarchical structure, may also contain associative relationships, or they may comprise unstructured lists.

Controlled Vocabularies is a term often used synonymously with taxonomies and thesauri. In a controlled vocabulary every entity must be disambiguated with a unique label. Controlled vocabularies are also characterised by formal policies and procedures governing the curation of the vocabulary.

Schemes are discrete ontologies (schema and taxonomy) compartmentalized by knowledge domain (e.g., topics, products, markets, brands, etc.), or usage and ownership (e.g., corporate, department X, division Y, etc.). Discrete schemes can standalone or be interconnected hierarchically and associatively to form a cohesive multi-domain Knowledge Organization System.

See also section Managing Multiple Taxonomy Schemes.

For example, SKOS[1] (Simple Knowledge Organization System) is a semantic schema for building taxonomies. Without any specific concepts SKOS is just a vocabulary of class, property and relationship types such as: skos:concept, skos:prefLabel, skos:definition, skos:broader and skos:narrower. Once a semantic schema is populated with a taxonomy of specific instances of concepts, classes and individuals it becomes a complete ontology. These specific concepts, classes and individuals may be used to name and describe abstract concepts and concrete entities. The taxonomy uses the logical structure of the semantic schema to describe things and their relationship to one another according to a formal semantic model. In a controlled vocabulary each specific concept, class or entity must be unambiguously named.