How to Build a Knowledge Graph from Podcasts

A knowledge graph is a structured network of entities and their relationships. Unlike a flat database or a simple list, a knowledge graph captures how things connect: which experts discuss which topics, which claims are supported or contradicted by other claims, and which organizations are linked to specific research areas. This relational structure makes it possible to discover insights that are invisible in isolated content.

Podcasts are one of the richest sources of expert knowledge available, yet this knowledge is locked in linear audio streams. Building a knowledge graph from podcast content unlocks that knowledge by extracting entities, mapping their relationships, and making the entire network searchable and explorable. The result is a living map of expertise that grows more valuable with every episode processed.

VeriDive's DeepLink module automates the construction of knowledge graphs from spoken content. It extracts over 20 types of Smart Objects from transcripts, identifies relationships between them, and builds a graph that you can navigate visually, query semantically, or integrate into your own research workflows.

Every knowledge graph is built from three fundamental components. Entities are the nodes: people, organizations, concepts, products, events, and other named things mentioned in podcast content. Relationships are the edges connecting those entities: "works at," "disagrees with," "recommends," "founded," and so on. Claims are assertions made by speakers that can be tracked, verified, and cross-referenced.

VeriDive's Smart Objects system recognizes and extracts these building blocks automatically. When a podcast guest says "Dr. Sarah Chen at Stanford published a study showing that meditation reduces cortisol by 25%," the system extracts the person (Dr. Sarah Chen), the organization (Stanford), the topic (meditation), the claim (reduces cortisol by 25%), and the relationship between them. All of this happens without manual tagging or annotation.

The power of a knowledge graph comes from connections across sources. A single podcast episode might mention a researcher, a company, and a technology. When you process hundreds of episodes, the same entities appear in different contexts, revealing patterns that no single episode could show. You might discover that two seemingly unrelated experts reference the same foundational research, or that a startup mentioned briefly on one podcast is led by an expert featured extensively on another.

DeepLink builds these cross-episode connections automatically. As new content is processed and indexed, the graph grows organically, with new nodes and edges appearing as new entities and relationships are discovered. You can explore the graph to see how your topic of interest connects to related areas you might not have considered.

This emergent structure is particularly valuable for interdisciplinary research. Breakthroughs often happen at the intersection of fields, and a knowledge graph built from diverse podcast sources can reveal those intersections in ways that siloed, field-specific research cannot.

Once built, a knowledge graph can be explored in multiple ways. Visual navigation lets you start at any node and follow connections outward, discovering related entities and the content that connects them. Semantic querying through DeepContext lets you ask natural language questions that are answered using the graph's structure, not just keyword matching.

For example, you could ask "Which experts on artificial intelligence have also discussed ethical concerns about facial recognition?" The graph would identify AI experts mentioned across your processed content and filter for those who are also connected to facial recognition ethics, returning the specific episodes and timestamps where those connections exist.

A knowledge graph is not a one-time project. Its value increases as you add more content. Set up DeepWatch agents to monitor key podcast feeds and YouTube channels, ensuring new episodes are automatically processed and integrated into your graph. Over time, you build a comprehensive, living knowledge base that reflects the current state of expert discourse on your topics of interest.

Periodic review is also important. As your graph grows, check for entities that should be merged (the same person referenced by different names), relationships that need updating (someone changed organizations), and new topic clusters that have emerged. VeriDive's entity resolution features help automate much of this maintenance, but occasional human review ensures the highest quality.