Unlock the Potential of Open AI in Smart Manufacturing
3.2 CONCEPTUAL ARCHITECTURE
Figure
3-3 : AI-driven knowledge management and intelligent search 5 .
The above workflow occurs in the near-real time where a user sends a query to the enterprise knowledge vault .
For example , an employee of a manufacturing company is searching for specific information about a machine part on the company portal .
The query is first processed by an intent recognizer like conversational language understanding . The relevant entities or concepts in the user query are used to select and present a subset of documents from a knowledge base that ' s populated offline ( in this case , the company ' s knowledge base database ).
The output is fed into a search and analysis engine like Cognitive Search , which filters the relevant documents to return a document set of hundreds instead of thousands or tens of thousands .
The user query is applied again on a search endpoint to the Intelligent Cognitive Search engine to rank the retrieved document set in order of relevance ( page ranking ). The highest-ranked document is selected . The selected document is scanned for relevant sentences . This scanning process uses either a coarse method , like extracting all sentences that contain the user query , or a more sophisticated method , like GPT-3 embeddings , to find semantically similar material in the document .
5
https :// learn . microsoft . com / en-us / azure / ai-services / language-service / summarization / how-to / document-summarization
Journal of Innovation 51