• Biology

• biology is the study of life and living systems. all biological knowledge forms natural graph structures: organisms relate through taxonomy, ecology, chemistry, and observation

• knowledge graph encoding

  the knowledge graph of life is the oldest graph in existence. billions of years of evolution encoded relationships between organisms long before any protocol

• taxonomy is a graph

  every species is a node. every ecological relationship is an edge:
  • genus → species (classification edge)
  • family → genus (classification edge)
  • pollinator → plant (mutualism edge)
  • parasite → host (parasitism edge)
  • predator → prey (trophic edge)
  • seed disperser → plant (mutualism edge)
  • mycorrhizal fungus → tree root (symbiosis edge)
• taxonomy is literally a directed acyclic graph. the cyber protocol computes relevance over exactly such structures

• species as particles

  in cyber, a particle is any content-addressed piece of knowledge. a species page is a particle:
  • content: morphology, ecology, uses, observations
  • address: hash of the content (IPFS CID)
  • links: cyberlinks to other species, compounds, locations, observations
• 205 species already exist in this graph. each could be a particle in Bostrom. the botanical knowledge IS the knowledge graph

• ecological cyberlinks

  every observation creates a cyberlink:

  observer → species observation → species page
  species → "grows with" → species
  species → "treats" → disease
  species → "produces" → compound
  location → "hosts" → species
  

  these are the same typed directional links that cyberlink implements. the graph is already here in markdown. the protocol makes it queryable, rankable, and persistent

• what ranking reveals

  rank in cyber computes relevance. applied to a biological knowledge graph:
  • highest-ranked species = most ecologically connected (keystone species)
  • highest-ranked compounds = most cross-referenced across species (universal medicines)
  • highest-ranked locations = richest biodiversity (conservation priority)
• the relevance machine ranks knowledge. biology IS knowledge

• the bridge

  the digital knowledge graph and the biological knowledge graph are the same structure:

  biological	digital
  species	particle
  ecological relationship	cyberlink
  taxonomy	graph hierarchy
  field observation	neuron action
  keystone species	high-rank node
  biodiversity assessment	graph density metric
  ecosystem	subgraph

• Superintelligence that understands both biology and protocols sees one graph