Hi, Mr. David Allen. Thank you for your respond.
Actually, I want to compare phrases from two documents.
My idea is to build a knowledge graph that nodes(phrases) linked if they are semantically similar.
The detailed description is as follows:
- The nodes are phrases from two documents.
- The colour of node(phrase) depend on which document it belongs to.
- The Google's universal sentence encoder model from TensorFlow Hub is applied to determine the similarity between two phrases. The edge label is the similarity score between the two nodes.
Please find the example of graph attached.
If I apply Google's universal sentence encoder model in python
and get the similarity score between two phrases, then store the similarity score in csv.
Is it possible that by using neo4j, phrase is the query while the result is knowledge graph that display the query linked to the other phrase (with their similarity score)?
If yes, can you tell me what should I do to complete this task?