Modeling knowledge

Semaphore's ability to deliver intelligent classification, metadata enrichment, and AI grounding begins with its robust knowledge modelling framework. At its core, Semaphore enables organizations to build and manage semantic models that represent the concepts, relationships, and rules that define their domain knowledge. These models are not static dictionaries---they are dynamic, governed, multilingual, and deeply integrated into enterprise workflows.

High-Level Overview: Semantic Modelling in Semaphore

Semaphore supports the creation and management of semantic models using open standards like SKOS-XL (Simple Knowledge Organization System eXtended) and OWL (Web Ontology Language). These models serve as the foundation for:

• Consistent metadata tagging

• Automated classification

• Fact extraction

• AI grounding

• Search and discovery

The modelling process is managed through the Knowledge Model Management (KMM) module, which provides a collaborative, governed environment for building and evolving these models.

SKOS-XL Taxonomies and OWL Ontologies

Taxonomies

• Hierarchical structures that organize concepts from general to specific.

• Example: "Regulation" → "Data Privacy" → "GDPR" → "Right to Erasure".

Ontologies

• Richer semantic structures that define not just hierarchy, but also associative and equivalence relationships.

• Example: "Customer Complaint" is related to "Product Defect" and equivalent to "Service Issue" in some contexts.

Multilingual Labels

• Each concept can have multiple labels:

  • Preferred terms

  • Synonyms

  • Acronyms

  • Translations

• These are managed using language packs, enabling global deployments.

Constraints and Rules

Semaphore allows users to define logical constraints and classification rules that govern how content is tagged and interpreted.

Constraints

• Define valid relationships between concepts.

• Prevent inconsistent or illogical tagging (e.g., "Invoice" cannot be a subtype of "Employee").

Classification Rules

• Written in a deterministic, explainable format.

• Can include:

  • Keyword patterns

  • Proximity logic

  • Metadata conditions

  • Language-specific variants

These rules are executed by the Classification and Language Services (CLS) engine, which applies them to content in real time or batch mode.

Model Lifecycle Management with KMM

The KMM module is the governance layer of Semaphore's modelling architecture. It supports:

• Collaborative authoring: Multiple users can contribute to model development.

• Version control: Track changes and roll back if needed.

• Review workflows: Ensure that new concepts and rules are approved before publication.

• Publishing: Deploy models to runtime environments (CLS) for classification and enrichment.

KMM ensures that semantic models are not only accurate but also aligned with business goals, regulatory requirements, and operational needs.