AI knowledge graphs reshape legal discovery

Litigation tools are evolving beyond basic search

The intersection of artificial intelligence and legal practice continues to reshape how attorneys approach complex litigation. Tom Smoker's insights on knowledge graphs in litigation agents highlight a significant shift from traditional document review to sophisticated knowledge networks that can transform legal discovery. This technology promises to move legal professionals from drowning in documents to surfacing meaningful connections that might otherwise remain hidden.

Key innovations in litigation AI

• Knowledge graphs create relationship networks rather than simple document collections, allowing attorneys to see connections between entities, events, and facts that traditional search cannot reveal
• Structured vs. unstructured knowledge extraction represents the evolution from basic information retrieval to complex relationship mapping that reflects how humans actually understand case narratives
• Natural language interfaces enable lawyers to interact with complex case data through conversational queries instead of Boolean search terms, making the technology more accessible

Why knowledge graphs matter now

The most compelling aspect of this technology is how it mirrors human cognition. Traditional legal search tools operate fundamentally differently from how attorneys actually think about cases. Lawyers don't naturally organize case knowledge as document collections—they think in terms of interconnected relationships between people, events, motivations, and chronologies. Knowledge graphs finally align the technology with attorneys' mental models.

This shift comes at a critical juncture for the legal industry. As case data volumes continue to explode, traditional document review approaches have reached their breaking point. The average commercial litigation case now involves hundreds of thousands of documents—a scale that makes comprehensive manual review functionally impossible. Knowledge graph technology offers a way to maintain quality while managing this data explosion.

Beyond the presentation: Real-world applications

While Smoker outlines the technical foundations, I've observed several practical applications worth noting. Consider how Relativity, a major e-discovery platform, has implemented knowledge graph capabilities to help identify key players in antitrust cases. Their system can automatically surface communication patterns between executives that might indicate collusion—relationships that might take weeks to discover manually.

The technology also creates opportunities for smaller firms to compete with larger practices. Traditionally, document-intensive cases required armies of associates or contract attorneys for effective review. Knowledge graph tools level this playing field by automating relationship mapping, allowing boutique firms to handle complex litigation with smaller teams.

For implementation, legal teams should consider a graduated approach