Resources
Applications and Use Cases of Natural Language Processing (NLP)
Main takeaways Introduction Natural language processing becomes most useful when language is connected to a specific task, such as interpreting a voice command, extracting clinical information, or analyzing customer sentiment.
read moreNatural Language Processing (NLP) Explained: Foundations, Architecture, Applications, and Code Implementation
Main takeaways Introduction Processing human language is difficult for machines because meaning depends on context, grammar, intent, domain knowledge, and ambiguity. Words can have multiple meanings, entities can have different
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Graphwise Talk #4: 5 Patterns of Enterprise AI Failure: How to Spot Yours?
In our fourth Graphwise Talk, data and AI practitioner Panos Alexopoulos and Jim Buonocore of #EPAM_Systems join Graphwise to unpack why enterprise AI projects stall: the failure patterns behind stuck pilots, why scaling is a data problem in disguise, and what it takes to build AI that compounds instead of stalling out.
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The Context Layer: The Emerging Stack for Context-Aware AI
Watch the replay of our Data Science Connect roundtable on the emerging context layer, then a Q&A with Andreas Blumauer.
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What is a LLM Hallucination?
A Large Language Model (LLM) hallucination is when a LLM generates factually incorrect or senseless information that looks grammatically correct and feels confident.
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Two Paths to Self-Improving AI Agents and Why One Works
Disconnected data is silently breaking enterprise AI agents. Alan Morrison compares two fixes: GraphRAG vs. recursive self-improvement, and what each means for your team.
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Graphwise Talk#3: Under the Hood of Trustworthy AI: Memory, Meaning, Infrastructure
In our third Graphwise Talk, ontologist Kurt Cagle joins Graphwise to get under the hood of trustworthy AI — how ontologies encode meaning, how semantic memory makes AI auditable, and what it actually takes to build systems that can explain themselves.
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Making Product Knowledge AI-Ready: Lessons from KAESER
Product data locked in SAP and siloed systems stops AI initiatives in their tracks. KAESER and PANTOPIX reveal how they built a unified semantic backbone to turn complex product knowledge into a live, AI-ready application.
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AI-Assisted Taxonomy Creation: Tools, Workflows, and Where Do Humans Fit In?
AI is changing how taxonomies are built, but what does that actually look like in day-to-day practice? This webinar reveals where AI speeds up taxonomy building, where human expertise is non-negotiable, and how both collaborate effectively.
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The Missing Layer Between Scientific Data and Breakthrough Ideas
R&D teams spend too much time searching systems instead of making breakthroughs. Graphwise and Datavid show how a semantic backbone unifies fragmented research data without requiring a costly infrastructure overhaul.
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The Enterprise Semantic Backbone: A Foundation for Reliable and Scalable Agentic AI
Learn how a Enterprise Semantic Backbone is a good foundation for reliable and scalable Agentic AI.
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Graphwise Platform: Everything Between Your Data and Your AI
Platform Pulse is a quarterly webinar series about the Graphwise platform, where we walk you what it is, how the pieces fit together, and close with a live end-to-end demonstration of the full pipeline from raw data to AI response.
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