Rule-Based Extraction and Entity Recognition
Using rule-based extraction and enrichment, it is possible to easily and with a large amount of flexibility, extract different entities based on your own defined rules.
The rule-based extraction is available at the very heart of the Mindbreeze semantic pipeline – meaning that extracted entities can either be used to enrich the search experience or be used in combination with our other services to perform more complex tasks such as lookups or synthesize metadata.
Adding rule-based extraction is very simple within the configuration settings of your Mindbreeze InSpire management center. Pattern extraction rules in the form of regular expressions can be defined, and entities can be extracted from anywhere within the indexed document or from a specific location. The exact format of the extracted entity can also be defined.
Here is a quick video tutorial for a clearer and visual representation of what this looks like:
Curious to learn more about rule-based extraction and entity recognition? We have two previous blog posts that may help!
Latest Blogs
How MCP Connects AI to Your Business Information
The Model Context Protocol, commonly called MCP, provides a standard way for AI applications to connect with external information and tools. It gives assistants and agents a consistent way to request help from systems outside the AI model.
How an AI Agent Uses MCP to Complete a Business Task
A customer reports that a production system has failed for the third time in two months. A service employee needs to review the customer’s coverage, understand the earlier incidents, find current technical guidance, and decide whether the issue should be escalated.