Knowledge Graph

The Wisdom Tree in depth - atoms, evidence chains, and the conflict detection system that keeps knowledge accurate automatically.

What Is an Atom?

An atom is the smallest unit of knowledge that can stand alone. Not a paragraph, not a document - a single, falsifiable claim. The discipline of atomicity is what makes knowledge searchable, linkable, and worth maintaining.

Good atoms:

• Make exactly one claim: "API p99 latency is 180ms at 1,000 RPS"
• Are self-contained - readable without surrounding context
• Can be confirmed or refuted by evidence
• Have the right type (DATA, LEARNING, DECISION, or PRINCIPLE)

Bad atoms:

• Make multiple claims in one statement
• Contain vague language ("sometimes", "might", "could be better")
• Duplicate another atom already in the graph

The Four Atom Types

Each type encodes the atom's epistemic role - how certain it is and how it was derived. The types form a derivation chain from raw observation to distilled wisdom:

flowchart LR
  D["DATA<br/>Raw measurement<br/>or observation"]
  L["LEARNING<br/>Pattern synthesized<br/>from multiple data points"]
  DE["DECISION<br/>Committed choice<br/>with rationale"]
  P["PRINCIPLE<br/>Guiding rule derived<br/>from repeated decisions"]
  D -->|DERIVES_FROM| L -->|DERIVES_FROM| DE -->|DERIVES_FROM| P

Connecting Knowledge

Atoms are not isolated notes. You can link them to each other and to strategy nodes, building a connected graph where every decision traces back to evidence, and every observation connects to the goals it informs. The more connections you create, the better Momental's search and conflict detection work.

Evidence Chains

The most valuable knowledge graphs have evidence chains: a DECISION linked back to the LEARNING that justifies it, which links back to the DATA that prompted the insight. When a decision exists without documented evidence, Momental flags it as a gap worth filling.

flowchart TD
  D1["DATA: Checkout abandonment 67%"]
  D2["DATA: 84% of abandoners cited surprise shipping cost"]
  L["LEARNING: Users abandon when shipping costs appear late"]
  DE["DECISION: Show estimated shipping cost before payment step"]
  P["PRINCIPLE: No surprise costs - ever"]

  D1 --> L
  D2 --> L
  L --> DE
  DE --> P

Every node in this chain is independently searchable. An agent working on the checkout flow can search for DECISIONS about checkout and find this, along with the evidence trail that justifies it - without reading any documents.

Knowledge Stays Current

Knowledge ages. Momental keeps the graph current automatically - recent observations rank higher than older ones in search results. Old decisions get flagged for review when the evidence they were based on has been updated. You don't have to maintain freshness manually.

Conflict Detection

Every time a new atom is added, Momental automatically checks it against existing knowledge for contradictions. If two atoms say opposing things - or if a new observation invalidates an older decision - the conflict is flagged and routed to your team for review.

High-confidence conflicts are surfaced immediately. Ambiguous cases are analyzed further before surfacing, so you don't get buried in noise.

To manage conflicts, see Conflicts & Gaps. To understand how Momental monitors for these signals continuously, see Autonomy & agents.

Gap Detection

Momental also scans for missing knowledge - areas where reasoning should exist but doesn't. Examples: a decision without documented evidence behind it, topics with asymmetric coverage, or knowledge that implies a policy violation.

Detected gaps appear in health and can be assigned to agents to fill. You can trigger a scan on demand with health.