Knowledge graph

Chive uses a community-curated knowledge graph to classify and connect scholarly works. This Wikipedia-style approach allows the research community to build and maintain a structured taxonomy of academic fields.

Overview

The knowledge graph serves three purposes:

1. Discovery: Find related eprints through field classifications
2. Context: Understand how a work fits into broader research areas
3. Navigation: Browse eprints by field, subfield, or topic

Field nodes

A field node represents an academic discipline, subdiscipline, or topic. Each field has:

Property	Description
label	Human-readable name (e.g., "Algebraic Geometry")
description	Brief explanation of the field's scope
alternateLabels	Alternative names (e.g., "Algebraic Geometry" = "AG")
externalIds	Links to Wikidata, Library of Congress, etc.

Hierarchical and associative relationships are represented as edges rather than embedded arrays:

Edge relation	Description
broader	Parent categories this field belongs to
narrower	Child specializations within this field
related	Fields with conceptual overlap

Field relationships

Fields connect through three relationship types:

PMEST classification

Beyond hierarchical fields, Chive uses PMEST (Personality, Matter, Energy, Space, Time) faceted classification. This system allows filtering across orthogonal dimensions:

Facet	Meaning	Example
Personality	Core subject	"Quantum mechanics"
Matter	Material or substance	"Carbon nanotubes"
Energy	Process or action	"Oxidation"
Space	Geographic scope	"Arctic regions"
Time	Temporal scope	"Holocene"

A single eprint can be classified across multiple facets:

Eprint: "Climate-Driven Carbon Nanotube Degradation in Arctic Soils"

Facets:
  Personality: Environmental Chemistry, Materials Science
  Matter: Carbon nanotubes, Soil
  Energy: Degradation, Climate change
  Space: Arctic
  Time: Contemporary (2020-present)

Facet search

Users can combine facets to narrow searches:

GET /xrpc/pub.chive.graph.browseFaceted?
  personality=materials-science&
  matter=carbon-nanotubes&
  space=arctic

Authority records

Authority records ensure consistency across the knowledge graph. They're like library catalog entries for concepts, represented as pub.chive.graph.node:

// Example: "Quantum Computing" node
{
  "$type": "pub.chive.graph.node",
  "id": "quantum-computing",
  "kind": "object",
  "subkind": "field",
  "label": "Quantum Computing",
  "alternateLabels": [
    "Quantum Computation",
    "QC"
  ],
  "description": "Computational paradigm using quantum-mechanical phenomena",
  "externalIds": [
    { "source": "wikidata", "value": "Q339" },
    { "source": "lcsh", "value": "sh2008010405" },
    { "source": "viaf", "value": "168470861" }
  ],
  "status": "established",
  "createdAt": "2025-01-15T10:30:00Z"
}

// Relationships are stored as separate edge records
// pub.chive.graph.edge with relationSlug: "broader" → "Computer Science", "Quantum Mechanics"
// pub.chive.graph.edge with relationSlug: "narrower" → "Quantum Error Correction", "Quantum Algorithms"
// pub.chive.graph.edge with relationSlug: "related" → "Quantum Information Theory"

Authority records link to external controlled vocabularies:

Vocabulary	Purpose
Wikidata	Multilingual structured knowledge
LCSH	Library of Congress Subject Headings
VIAF	Virtual International Authority File
FAST	Faceted Application of Subject Terminology

Reconciliation

When users tag eprints, Chive reconciles tags against authority records:

This prevents fragmentation ("quantum computing" vs "Quantum Computation" vs "QC" all map to the same concept).

Community governance

The knowledge graph uses Wikipedia-style moderation. Users can propose new fields or changes, discuss proposals in threaded comments, and vote on whether to accept them. Proposals require weighted community approval before changes take effect.

For details on how proposals are evaluated, including voter roles and approval thresholds, see Voting system.

User tags vs. authority terms

Chive distinguishes between user-generated tags and authority-controlled terms:

User tags	Authority terms
Free-form text	Controlled vocabulary
Personal organization	Community consensus
No voting required	Proposal + voting
May be reconciled	Canonical concepts

Users can tag eprints freely. Popular tags may be promoted to authority terms through a two-stage process:

1. Automatic nomination: Tag used on 10+ eprints by 3+ users
2. Community vote: Standard proposal process

Graph algorithms

The knowledge graph enables discovery features:

Citation analysis

Find papers that:
  - Cite foundational works in the field
  - Bridge multiple subfields
  - Introduce new connections

Semantic similarity

Given an eprint about "quantum error correction":
  - Find semantically similar eprints
  - Suggest related fields to explore
  - Identify key authors in adjacent areas

Field evolution

Track how fields change over time:
  - New subfields emerging
  - Fields merging or splitting
  - Terminology shifts

Integration with search

The knowledge graph enhances search in several ways:

Expansion

A search for "machine learning" automatically includes:

• Narrow terms: "deep learning", "neural networks"
• Related terms: "artificial intelligence", "statistical learning"

Disambiguation

A search for "network" prompts:

• Computer networks?
• Neural networks?
• Social networks?
• Network science?

Faceted browsing

Filter results by any PMEST dimension while staying within a field:

Field: Machine Learning
  Filter by Matter: Medical imaging
  Filter by Time: Last 5 years
  Filter by Space: [any]

Wikidata integration

Chive synchronizes with Wikidata to:

1. Import established classifications
2. Link local concepts to global identifiers
3. Contribute new academic concepts back

# Example SPARQL query to find related concepts
SELECT ?item ?itemLabel WHERE {
  wd:Q339 wdt:P279* ?item .  # Q339 = Quantum computing
  ?item wdt:P31 wd:Q11862829 .  # Instance of academic discipline
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en" }
}

API endpoints

Endpoint	Purpose
pub.chive.graph.getNode	Get node details by ID
pub.chive.graph.listNodes	List nodes (paginated, filtered)
pub.chive.graph.searchNodes	Search nodes by label
pub.chive.graph.getField	Get field details (convenience)
pub.chive.graph.listFields	List fields (paginated)
pub.chive.graph.listEdges	List edges for a node
pub.chive.graph.browseFaceted	Faceted search
pub.chive.graph.getFieldEprints	Eprints in a field

Next steps

• Data sovereignty: How your data stays yours
• Peer review: Review and endorse eprints
• Governance: How decisions are made