Overview
A knowledge graph is a graph-structured knowledge base used to represent and operate on data by explicitly modeling entities and the relationships between them. Unlike traditional databases, which emphasize discrete entities and tabular schemas, knowledge graphs prioritize connections, semantics, and contextual richness.
“A knowledge graph is a digital structure that represents knowledge as concepts and the relationships between them (facts). It can include an ontology that allows both humans and machines to understand and reason about its contents.” — Wikipedia
Core Concepts
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Entities: Objects, people, places, concepts
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Relationships: Semantic links between entities (e.g., “livesIn”, “foundedBy”)
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Triples: Subject-Predicate-Object statements (e.g., “Ada Lovelace” — “contributedTo” — “Computing”)
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Ontologies: Formal definitions of types and relationships (e.g., RDF, OWL)
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Reasoning: Inferring new knowledge from existing triples via logic
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Embedding: Vector-based representation of nodes/edges for ML applications
Historical Context
Knowledge graphs emerged from semantic network research in the 1970s, evolving through:
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WordNet (1985): Lexical graph of semantic word relationships
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Freebase & DBpedia (2007): Structured knowledge from Wikipedia
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Google Knowledge Graph (2012): Mainstream adoption for search enhancement
Applications
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Search & Recommendations (Google, Amazon, Facebook)
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Scientific Research (genomics, proteomics)
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Virtual Assistants (Siri, Alexa)
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Personal Knowledge Management (Obsidian, Roam Research)
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Decentralized Knowledge Commons (Wikidata, DeSci)
Discourse Graphs: Civic & Scientific Knowledge Commons
A specialized form of knowledge graph, discourse graphs model:
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Questions, claims, evidence, and their logical links
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Structured debate and collective intelligence
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Used for civic deliberation, scientific inquiry, and protocol evolution
Discourse graphs are essential for:
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Mapping collective knowledge and emergent insights
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Supporting decentralized science (DeSci) and civic learning loops
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Creating open protocol libraries and evolving governance tools
“Discourse graphs enable protocol implementation to generate evidence and feedback loops that inform new hypotheses and adaptations across regions.” — Discourse Graphs for Civic Knowledge Commons, Apr 2025
From Separation to Relationality
Modern relational paradigms (linked data, semantic web, graph DBs) reflect a civilizational shift:
| Paradigm Shift | From | To |
|---|---|---|
| Knowledge Representation | Tables, silos | Graphs, linked data |
| Data Ownership | Centralized extraction | Federated, commons-based |
| Epistemology | Objective fragmentation | Participatory relationality |
| Knowledge Stewardship | Consumption | Co-creation & commoning |
Knowledge graphs support this transition by:
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Structuring semantic relationships
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Facilitating cross-domain synthesis
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Enabling AI-human collaboration
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Anchoring data in place-based and value-aligned contexts
Technical Highlights
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Standards: RDF, OWL, JSON-LD, SPARQL
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Tools: Neo4j, GraphDB, TerminusDB, OriginTrail
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Use Cases: Personal wikis, bioregional mapping, protocol development, research synthesis
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Integrations: Web3, AI agents, semantic search, decentralized storage