Knowledge Extraction from Technical Documentation
NLP-powered knowledge graph construction from a large corpus of technical documentation with semantic search capabilities.
Challenge
An international industrial holding had accumulated a vast body of technical documentation -- manuals, specifications, regulations, and test reports. Searching for the right information took hours, and when experienced specialists left the company, their expertise left with them. The organization was losing its competitive edge due to inefficient knowledge management.
Solution
The system analyzes technical documentation, extracts key entities (parameters, materials, procedures, constraints), identifies relationships between them, and builds a structured knowledge base -- a knowledge graph. Engineers ask questions in natural language and receive precise answers with source references.
Results
Technologies
Approach
Documentation inventory and ontology definition
Classifying documents by type, defining key entities and relationships for knowledge graph construction.
Entity extraction pipeline development
Building an NLP model for automated extraction of parameters, materials, procedures, and constraints from text.
Knowledge graph construction
Building a structured knowledge base with entity relationships and navigation capabilities.
Search interface development
Building a natural language search interface that delivers precise answers with source references.
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