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AI/ML

BioAgents: AI-Powered Knowledge Extraction for Research Papers

Discover how our AI agents automatically extract metadata, identify entities, and build knowledge graphs from research documents.

Ankita Choudhary
November 28, 2025
7 min read

Introducing BioAgents

BioAgents is our AI-powered system for automated knowledge extraction from research documents. Built on the Eliza OS agent framework, it transforms unstructured research papers into structured, queryable knowledge.

What BioAgents Can Do

1. Metadata Extraction

BioAgents automatically extracts:

  • Bibliographic data: Authors, titles, abstracts, keywords
  • Institutional affiliations: Universities, research centers, funding bodies
  • Publication details: DOIs, journal information, publication dates

2. Named Entity Recognition

Our specialized models identify:

  • Biological entities: Genes, proteins, diseases, compounds
  • Research concepts: Methods, techniques, experimental conditions
  • Geographic and temporal references: Study locations, time periods

3. Relationship Extraction

BioAgents identifies connections between entities:

  • Gene-disease associations
  • Drug-target interactions
  • Author collaboration networks
  • Citation relationships

How It Works

Document inputPDF, DOCXText extractionGROBID, Apache TikaSection classificationAbstract, methods, resultsEntity recognitionBioBERT, SciBERTRelationship extractionGraph neural networksKnowledge graphRDF / JSON-LD
From document to knowledge graph

Using BioAgents

typescript
// Process a research paper
const result = await client.bioagents.process({
  cid: 'QmXg9Pp2ytZ14xgK35M6iTC2Vz6jR9zYgooNp2UHPTMnPN',
  options: {
    extractMetadata: true,
    identifyEntities: true,
    buildKnowledgeGraph: true
  }
});

// Access extracted entities
console.log(result.entities.genes);      // ['BRCA1', 'TP53', ...]
console.log(result.entities.diseases);   // ['breast cancer', ...]
console.log(result.entities.compounds);  // ['tamoxifen', ...]

// Query the knowledge graph
const related = await client.graph.query({
  sparql: `
    SELECT ?gene ?disease WHERE {
      ?gene bio:associatedWith ?disease .
      ?disease rdfs:label "breast cancer" .
    }
  `
});

Integration with DIDs

Every extracted knowledge graph is linked to the source document's DID, creating a verifiable chain of provenance:

json
{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "identifier": "did:bio:kg-12345",
  "derivedFrom": "did:bio:paper-67890",
  "dateCreated": "2025-11-28",
  "creator": "bioagents:v2.1"
}

Learn more in our BioAgents Architecture documentation.

Ready to Get Started?

Explore our documentation to learn how to integrate Ekayana into your research workflow.