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How textual search works in graph databases

A database, especially one full of long strings of unstructured text, is only as valuable as how easily you can search it and extract meaningful interpretations. That’s harder than ever with recent changes in how many organizations ask their teams to manage and learn from the data their functional area produces. Instead of waiting for Read the full article…

How natural language processing (NLP) works in graph databases

Most people in an organization have access to far more relevant text documents than they do neatly-organized sets of data. Whether they want to ask questions about customer sentiment or analyze a market’s demand for a potential new product or service, it’s often easier to read pages and pages of text-based research instead of finding Read the full article…

Text is nothing more than unstructured data. When you consider how much textual data your enterprise organization has – or has access to – it’s no surprise that 90% of your data isn’t analyzed. Traditional analytics can only analyze structured data. But no matter how much information your organization has stored in tables, rows, and Read the full article…