Build a Knowledge Graph from Podcasts and Interviews

A knowledge graph is a structured representation of entities (people, organizations, concepts, events) and the relationships between them. Unlike flat databases or document collections, knowledge graphs capture the web of connections that give information its context and meaning.

Spoken content, particularly podcasts and interviews, is one of the richest sources of relational information available. When an industry expert appears on a podcast, they reference other researchers, cite companies and products, describe methodologies, connect to historical events, and position ideas within broader conceptual frameworks. All of these connections are expressed naturally in conversation but are lost when the only output is a transcript or summary.

VERIDIVE's DeepLink module captures these connections systematically. Every entity mentioned in processed content is identified, categorized, and linked to other entities based on the relationships expressed in the source material. Over time, this produces a rich, multi-layered knowledge graph that reveals patterns, clusters, and connections invisible to any individual listener.

DeepLink operates on the structured output of VERIDIVE's processing pipeline. After content is transcribed, speakers are identified, and entities are extracted by Smart Objects, DeepLink analyzes the co-occurrence and contextual relationships between entities to build the knowledge graph.

The types of relationships DeepLink captures include:

• Person-to-person: Collaboration, mentorship, disagreement, co-authorship, and professional associations expressed in interviews
• Person-to-organization: Employment, advisory roles, investment relationships, and board memberships mentioned by speakers
• Concept-to-concept: Theoretical relationships, cause-and-effect chains, and dependency structures described in educational content
• Organization-to-organization: Partnerships, competitive relationships, supply chain connections, and investment relationships
• Claim-to-evidence: Links between assertions and the supporting evidence or citations speakers provide

Each relationship edge in the graph includes rich metadata: the source content, timestamp, speaker, confidence level, and relationship type. This means every connection in the graph is traceable back to the specific moment in the specific conversation where it was expressed, ensuring full provenance for every piece of linked knowledge.

The quality of a knowledge graph depends on the quality of its entity recognition. VERIDIVE's Smart Objects system recognizes and categorizes over 20 distinct entity types from spoken content:

• People: Speakers, referenced individuals, authors, researchers, executives
• Organizations: Companies, universities, research labs, government agencies, NGOs
• Products and technologies: Software, hardware, frameworks, tools, platforms
• Financial entities: Revenue figures, funding amounts, valuations, market sizes
• Scientific claims: Hypotheses, findings, methodologies, datasets
• Events: Conferences, product launches, regulatory actions, historical events
• Locations: Cities, countries, regions relevant to the discussion
• Time references: Dates, periods, deadlines, milestones
• Concepts and theories: Abstract ideas, frameworks, models, paradigms

Smart Objects do not just tag text with simple labels. They resolve entities across sources, meaning that "Alphabet," "Google," and "the parent company of YouTube" are recognized as referring to the same organization. This entity resolution is critical for building accurate knowledge graphs where the same real-world entity might be referenced differently by different speakers in different contexts.

DeepLink provides multiple ways to explore the knowledge graph:

Visual Graph Explorer

Navigate the graph visually, clicking on entities to see their connections, expanding nodes to reveal second and third-degree relationships, and filtering by entity type, time period, or source. The visual explorer is particularly useful for discovering unexpected connections, such as two seemingly unrelated companies that share a common advisor or technology dependency.

DeepContext Graph Queries

Use natural language through DeepContext to query the graph conversationally. Examples include: "What is the connection between researcher X and company Y?", "Show me all experts who have discussed both quantum computing and pharmaceutical research," or "Map the network of people who have appeared on these three podcast series."

Temporal Analysis

Filter the graph by time period to see how relationship networks evolve. Track when new connections emerge, when existing relationships strengthen or weaken, and when new entity clusters form. This temporal view is invaluable for understanding how fields, industries, and communities evolve over time through their spoken interactions.

Knowledge graphs built from spoken content serve diverse professional needs:

Academic Research

Map the intellectual landscape of a research field by processing conference talks, lecture series, and academic podcasts. Identify emerging sub-fields, track which researchers are bridging different domains, and discover collaborative clusters that suggest upcoming breakthroughs.

Journalism and Investigation

Build relationship maps between public figures, organizations, and events from their media appearances. Identify network connections that are not visible in any single source but emerge when hundreds of interviews are analyzed together.

Business Strategy

Map competitive landscapes, technology ecosystems, and industry expert networks from podcast and YouTube content. Identify potential partners, track thought leadership influence, and detect emerging market narratives before they reach mainstream awareness.

Personal Knowledge Management

Build a personal knowledge graph from the podcasts and lectures you consume. Over months, this graph becomes a personalized map of your intellectual interests, connecting ideas across domains and surfacing unexpected relationships between topics you have explored.