Introduction
- Last Updated: May 13, 2026
- 3 minute read
- Semaphore
- Documentation
The component nature of Semaphore means it is possible to implement Semaphore in any number of ways to suit the environment or provide the performance required in the implementation. This document discusses how to architect the solution such a way to suit your requirements including specific use cases and implementation details.
Semaphore components
Semaphore incudes these components:
Knowledge Model Management (KMM)
KMM is the foundation of Semaphore's semantic modeling capabilities. It allows information architects and subject matter experts to collaboratively design and manage taxonomies, ontologies, thesauri, and controlled vocabularies. These models are built using standards like SKOS-XL and OWL, and can include multilingual labels, synonyms, and hierarchical or associative relationships.
KMM supports:
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Ontology building
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Model building
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Version control: Track changes and manage model evolution over time.
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Workflow governance: Review and approval processes for publishing models.
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Constraint definition: Logical rules that ensure semantic consistency.
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Publishing: Models are deployed to CLS for runtime classification.
Classification and Language Services (CLS)
CLS is the runtime engine that applies semantic models to content. It performs:
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Rule-based classification: Deterministic tagging of documents using logical rules derived from the semantic model.
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Fact extraction: Identifies structured data (e.g., names, dates, relationships) from unstructured text using NLP and pattern recognition.
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Scoring and confidence: Assigns confidence levels to classifications, enabling threshold-based filtering.
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Quality assurance: Tools like the Document Analyzer and Precision & Recall Tool help validate and refine classification accuracy.
CLS is designed for high-throughput environments and can be deployed as a service to process content in real time or batch mode. It supports integration with enterprise search, content management, and analytics platforms.
Semantic Integration Services (SIS)
SIS provides the APIs and connectors that allow Semaphore to integrate with external systems. It ensures that metadata generated by CLS can be consumed by downstream applications and that content from various sources can be ingested for classification.
Key capabilities include:
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RESTful APIs: For programmatic access to classification, model metadata, and fact extraction services.
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Connectors: Pre-built integrations with platforms like SharePoint, OpenText, Documentum, and analytics tools.
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Metadata synchronization: Ensures that enriched metadata is pushed back into source systems or centralized repositories.
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Security and access control: Supports enterprise authentication and authorization standards.
SIS is critical for embedding Semaphore into enterprise workflows, enabling use cases like intelligent search, compliance automation, and AI grounding.
Integration with Enterprise Systems
Semaphore is designed to operate as a semantic layer across the enterprise. It connects with:
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Content Management Systems (CMS): To classify and enrich documents at ingestion or retrieval.
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SharePoint and Microsoft 365: For metadata tagging, governance, and Copilot integration.
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Analytics platforms: To enhance dashboards and reports with semantic context.
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AI tools like Copilot: To reduce hallucinations by grounding responses in structured knowledge.
This integration ensures that metadata is not only consistent and governed but also actionable across business processes.
Audience
This document is intended for individuals responsible for the architectural design of a Semaphore solution or are looking at architectural issues such as replication or migration. It is assumed that readers have a good understanding of operating system and network administration for the environment in which Semaphore is to be implemented.
Additional documentation available
The following documentation provides additional information that may be helpful to the reader: