How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations

How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations">

['In this tutorial, we will explore the process of generating knowledge graphs from plain text, conversations, and multiple source documents using kg-gen. The process begins with setting up the required dependencies and configuring a large language model (LLM) through LiteLLM. We then extract entities, predicates, and relationships from simple text.

As we progress, we work with longer passages using chunking and clustering, combine knowledge graphs from different sources, visualize graph structures, and analyze them using NetworkX. By the end of this tutorial, we will have built a complete workflow that turns unstructured text into an interpretable, searchable, visual, and exportable knowledge graph.', 'To get started, we install all the required libraries for knowledge graph generation, graph analytics, and visualization. We then import the core packages, including kg-gen, NetworkX, PyVis, Matplotlib, and display utilities for Colab.

We also configure the API key and initialize KGGen with the selected model, which enables us to start generating graphs from text.', 'We first test kg-gen on a simple family relationship example to extract entities, edges, and relations. We then move on to a longer AI-focused passage where we use chunking to handle larger text and clustering to merge similar entities or relationship types. We print the extracted graph components and inspect entity clusters to understand how the model organizes related concepts.

We also use a conversation-style input to show how kg-gen extracts structured relations from user-assistant messages.', 'We generate separate graphs from multiple text sources, aggregate them, and apply clustering to resolve related entities such as "Joe" and "Joseph." We also try the built-in visualization feature and display the generated HTML graph inside Colab. We then convert the generated knowledge graph into a NetworkX graph to enable deeper graph analytics. We calculate degree centrality, betweenness centrality, PageRank, predicate frequency, and community structure to identify important entities and relation patterns.', 'We create a custom PyVis visualization where nodes are sized by PageRank and colored by detected communities.

We build a simple knowledge graph lookup function that retrieves relations connected to a query term, such as "Transformer," "Anthropic," or "Stanford." We also inspect the two-hop neighborhood of an entity to understand nearby concepts in the graph. Finally, we export the knowledge graph as JSON and GraphML for reuse in tools such as Gephi and Cytoscape.', 'In conclusion, we have built a full knowledge graph generation pipeline that moves from basic extraction to advanced graph analysis and visualization. We used kg-gen to identify entities and relationships, apply clustering to merge similar concepts, aggregate graphs from multiple inputs, and convert the result into NetworkX for centrality, PageRank, community detection, and predicate analysis.