Fact Extraction

Fact extraction is the process of identifying and storing key claims, decisions, tasks, entities, and structured data from unstructured content—text, audio, conversations, documents. It transforms raw information into queryable facts: "Alice owns Task 123," "Project Alpha depends on Beta," "Decision made to use OAuth."

Fact extraction powers agent memory by converting messy, unstructured sources into structured knowledge that agents can reason over. Instead of searching through transcripts or documents, agents query extracted facts: "What decisions were made last week?" or "Who owns blocked tasks?"

The outcome is structured, queryable memory built automatically from unstructured sources—no manual data entry required.

Why it matters

• Converts unstructured to structured: Documents, meetings, and chats become queryable facts—agents reason over structure, not raw text.
• Enables automated memory building: Fact extraction runs continuously on new content—memory updates without human intervention.
• Powers decision and task tracking: "Decision made to migrate by Q2," "Task: implement OAuth"—agents track commitments and work automatically.
• Supports relationship discovery: Extraction identifies connections: "Alice mentioned Bob in context of Project X"—builds knowledge graphs.
• Reduces manual note-taking: Meeting transcripts automatically yield tasks, decisions, and action items—no need for human summaries.
• Improves search precision: Structured facts enable queries like "tasks assigned to Alice" vs. keyword search for "Alice."

How it works

Fact extraction operates through parsing, extraction, and storage:

• Ingestion → Content enters the system: documents (PDFs, emails), conversations (Slack, meetings), structured data (Jira, calendar events).
• Content Parsing → Text is extracted from various formats. Audio is transcribed. Structured data is normalized.
• Entity Extraction → NLP models identify entities: people (Alice), companies (Acme), projects (Alpha), dates (Nov 3), tasks (implement auth).
• Relationship Extraction → The system identifies connections: "Alice owns Task 123," "Project Alpha depends on Beta," "Decision made in Meeting X."
• Claim Extraction → Key statements are identified: "We will migrate to OAuth by Q2," "Alice is expert in authentication."
• Fact Structuring → Extracted information is converted into structured facts with schema: {subject: Alice, predicate: owns, object: Task 123, timestamp: Nov 3}.
• Storage and Linking → Facts are stored in the knowledge graph, linked to source documents, and indexed for retrieval.

This pipeline builds structured memory from unstructured sources automatically.

Comparison & confusion to avoid

Examples & uses

Meeting minutes to structured facts
Transcript: "Alice will implement OAuth by Nov 15. Bob raised concerns about backward compatibility. Decision: we'll support both auth methods during migration." Extracted facts: {task: "implement OAuth", owner: Alice, deadline: Nov 15}, {decision: "support both auth methods", context: migration, decider: team}.

Email to tasks and decisions
Email: "Hi team, I've decided we should migrate to the new API. Can someone own testing? Thanks, Alice." Extracted facts: {decision: "migrate to new API", decider: Alice, date: Nov 3}, {task: "own API testing", status: unassigned}.

Project documentation to dependency graph
Document: "Project Alpha requires completion of Beta. Beta is blocked on infrastructure work owned by DevOps team." Extracted facts: {project: Alpha, depends_on: Beta}, {project: Beta, status: blocked, blocker: infrastructure work, owner: DevOps}.

Best practices

• Validate extracted facts: Extraction models have errors—implement confidence scoring and human review for critical facts.
• Link facts to sources: Store provenance (which document, paragraph, timestamp) so users can verify and trust extracted facts.
• Use domain-specific extraction: Generic models miss domain nuances—fine-tune or use schema-guided extraction for your use case.
• Extract temporal context: "Alice owns X" should include "as of Nov 3" or "from Oct 1 to Nov 5"—facts change over time.
• Support incremental extraction: When new content arrives, extract facts and update the knowledge graph—don't reprocess everything.
• Enable human corrections: Allow users to fix extraction errors—agents learn from feedback and improve over time.

Common pitfalls

• Trusting extraction blindly: Models make mistakes—implement confidence thresholds and review workflows for high-stakes facts.
• No provenance tracking: If you can't trace a fact back to its source, users won't trust it—always link to origin.
• Static extraction: Facts evolve—"Alice owned Task X" becomes "Bob owns Task X"—support fact updates and versioning.
• Over-extraction: Extracting every sentence creates noise—focus on salient facts (decisions, tasks, ownership, dependencies).
• Ignoring negation: "We will NOT use OAuth" vs. "We will use OAuth"—extraction must handle negation correctly.

See also

• Entity Linking — Resolving extracted entities to canonical identities
• Knowledge Graph — Structured storage for extracted facts
• Semantic Memory — Meaning-based memory built from facts
• Temporal Memory — Time-aware facts and state changes
• Agent Memory — Persistent context powered by fact extraction

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See how Graphlit implements Fact Extraction for knowledge graphs → Agent Memory Platform