Semantic Analytics
Leverage graph-based text mining and NLP to extract hidden concepts from unstructured data and enrich your enterprise knowledge graph
Graphwise Semantic Analytics is an advanced text mining and Natural Language Processing (NLP) tool—automating how enterprises analyze unstructured text and enrich it with context-aware metadata.
Unstructured data (PDFs, emails, reports) is inherently ambiguous, making it difficult for AI agents to process effectively.
Graphwise eliminates this blind spot by extracting actual concepts instead of simple text strings. By automatically linking text to your corporate taxonomy, it transforms massive document volumes into structured, AI-ready intelligence with human-like precision.
Extract meaning instead of strings
Graphwise Semantic Analytics extracts meaningful phrases to identify exactly what your content is about. This generates richer, highly precise metadata that mirrors your company’s unique domain.
- Automatically extract relevant concepts, specialized entities, timestamps, names, or locations
- Use advanced Named Entity Recognition (NER) to capture organizations, people, and locations
- Reveal hidden concepts that are not explicitly written in the text
- Detect the core focus of a document using domain-specific sentiment analysis and ML algorithms (SVM, Deep Learning, Naive Bayes, and more)
- Rely on corpus learning to determine a term’s actual significance to the text, rather than just counting how many times it appears
Overcome linguistic challenges with NLP.
Natural language is inherently complex and full of ambiguities, i.e. such as distinguishing “apple” the fruit from “Apple” the tech company. Graphwise eliminates the need for complex manual rules to solve these linguistic challenges.
- Use word sense disambiguation to resolve ambiguous terms based on local context of the term in a knowledge graph
- Analyze words using lemmatization techniques based on a dictionary (available in multiple languages)
Make your content intelligent with rich metadata.
Text mining backed by a centralized taxonomy ensures absolute consistency through controlled vocabularies and hierarchical structures.
- Harmonize text variations using preferred, alternative, and hidden labels and synonyms
- Automatically organize captured concepts into their correct classes and schemes for better categorization
- Unify your text annotations with standardized RDF data optimized for graph databases
- Use the enriched metadata to benefit from a semantic search engine
Our automated concept tagging serves as the basis of ready-made integrations like Graphwise for M365, Tridion Sites, and Adobe Experience Manager. Use Graphwise to take your CMS to the next level.
Other features you’ll love:
Pull relevant entities from a text and tag thousands of documents in less than an hour.
“Initially, it took the engineering team up to two weeks to edit basic terms or single tags. Because of Graphwise, however, this time window has shrunk to less than 24 hours!”
Dana Bublitz
Senior Information Architect , Microsoft Docs
Explore Our Success Stories
Expanding Editorial Output for the FIFA World Cup, Delivering 800+ Pages in Weeks
The BBC used semantic technology to To power its 2010 FIFA World Cup website to cut editorial costs and improve user experience.
Read more
Scaling Search and Content Governance with Semantic Technology
Microsoft Docs transformed millions of technical documents into a semantically enriched, scalable knowledge system.
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Transforming AI Reliability by Building Knowledge Graph-Powered Customer Support
Avalara used GraphRAG to establish a foundation for reliable, mission-critical AI applications in tax and financial services.
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Building a Collaborative Knowledge Hub for AI-Powered Predictive Intelligence
Sensing Clues implemented Graphwise’s Knowledge Management Suite to build vast organizational knowledge models.
Read moreTake a deep dive with an expert.
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