Creating a Knowledge Graph from Text for Named Entity Recognition in Python using Spacy

[tugbargn]

I'm working on a project to extract named entities from text data using Spacy in Python. I'd like to take it to the next level by creating a knowledge graph that represents the relationships between these entities.Here's what I've done so far:

Used Spacy's NER model to extract entities from text

Created a list of entities with their corresponding labels (e.g. PERSON, ORG, LOCATION)

But now I'm stuck on how to create a knowledge graph that represents the relationships between these entities. Can anyone share their experience or provide guidance on how to achieve this? Specifically, I'd love to know:

How to define the relationships between entities (e.g. PERSON works at ORG, LOCATION is part of COUNTRY)

How to represent the graph structure in Python (e.g. using NetworkX, Pandas, or another library)

How to visualize the graph to gain insights into the relationships between entities

Any help or pointers would be greatly appreciated!Thanks in advance for your responses!