Semaphore Filters
Apply meaning with Logic Filters™
Our Approach
Our Semantic AI approach analyzes structured and unstructured information to identify and extract hidden facts and relationships in content. Semantic AI incorporates Machine Learning (ML) and Natural Language Processing (NLP) using authoritative knowledge models to reveal corporate wisdom.
Key Capabilities
Semaphore processes content and extracts information using model-generated rulebases, NLP, ML, and fact extraction to give organizations the best of both worlds – precise and consistent results with the ease of automation. Semaphore automates manual processes for higher precision.
Semantic Integration & Enhancement
NLP, machine learning, and statistical techniques process information (i.e. content, documents, and data) and enrich with relevant metadata in multiple languages.
Classification Review Tools
Assess the quality of classification to drive consistency, identify anomalies and opportunities for model or classification refinement.
Fact Extraction Framework
A model-driven framework for business users to define accurate and robust business criteria for the extraction of required context.
Auto-Classification, Fact & Language Services
Classification and Language Services (CLS)
CLS processes enterprise information (content, documents, and data) leveraging NLP, Machine Learning strategies, and rules and algorithms generated from your semantic models managed in KMM, to classify and derive metadata values. CLS processes information assets (i.e. web page, PDF, or chunk of text) from any content management, enterprise search, workflow engine, or business application to assess relevant metadata values and pass the results to the requesting system. Includes classification analysis tools, Publisher, language service for entity identification, extraction capabilities, and text mining. Multi-language service is supported.
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Classification Analysis Tool (CAT) and Document Analyzer (DA)
As part of CLS, CAT & DA analyze individual documents and provide explainable classification results that allow you to make rule and model changes that increase the precision of classification output.
Classification Review Tool (CRT)
As a part of CLS, CRT examines the quality of classification results using sample content to identify inconsistencies, anomalies, and opportunities for model or classification strategy refinement.
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Fact Extraction Framework (FACTS)
Extend Semaphore out-of-the-box classification capabilities with a model-driven framework using NLP, existing knowledge models, and AI entity recognition. Subject matter experts and end users can define the context related to entities, ontology-based items, or free text, that turn them into facts. Extraction of information with context has significant value to the business.
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Find Out What Semaphore
Can Do for You
For more information on how we can help you
maximize the value of your data.