Knowledge Graph Integration

The Knowledge Graph integration enables rich, interconnected data representation across the Bio-DID-Seq and BioAgents ecosystem, supporting semantic queries and intelligent data discovery.

Overview

Knowledge Graphs provide a powerful way to represent research data as interconnected entities and relationships rather than isolated documents. The Bio-DID-Seq platform leverages knowledge graphs to:

• Enhance discoverability of research data
• Connect related biological entities across datasets
• Enable complex semantic queries
• Support AI powered knowledge extraction and reasoning

Knowledge Graph Architecture

1. Core Components

RDF Triple Store

The foundation of the knowledge graph is a Resource Description Framework (RDF) triple store that represents data as subject-predicate-object triples:

# Example triple representation
<http://example.org/gene/BRCA1> <http://example.org/relation/associatedWith> <http://example.org/disease/BreastCancer> .

JSON-LD Documents

Knowledge is also represented in JSON-LD format for compatibility with web standards:

{
  "@context": {
    "bio": "http://example.org/biology/",
    "schema": "http://schema.org/"
  },
  "@id": "bio:gene/BRCA1",
  "@type": "bio:Gene",
  "bio:name": "BRCA1",
  "bio:associatedWith": {
    "@id": "bio:disease/BreastCancer",
    "@type": "bio:Disease",
    "bio:name": "Breast Cancer"
  }
}

SPARQL Endpoint

A query interface allows complex semantic queries against the knowledge graph:

PREFIX bio: <http://example.org/biology/>

SELECT ?gene ?disease WHERE {
  ?gene bio:associatedWith ?disease .
  ?disease a bio:Disease .
  ?disease bio:relatedTo bio:CancerPathway .
}

2. Integration Points

Knowledge Extraction Process

1. Document Processing

BioAgents processes research papers to extract structured knowledge:

• Text Extraction: Convert PDF documents to machine readable text
• Section Identification: Identify abstract, methods, results, etc.
• Entity Recognition: Identify genes, proteins, diseases, etc.
• Relationship Extraction: Determine connections between entities

2. Knowledge Graph Integration

Query Capabilities

1. Basic SPARQL Queries

PREFIX bio: <http://bio-ontology.org/>

SELECT ?gene ?diseaseName WHERE {
  ?gene bio:associatedWith ?disease .
  ?disease a bio:Disease ;
           bio:name ?diseaseName .
  FILTER(CONTAINS(?diseaseName, "Alzheimer"))
}

2. Natural Language Queries

BioAgents supports natural language queries that are translated to SPARQL.

Example query: "What genes are associated with Alzheimer's disease that also interact with the APOE pathway?"

Use Cases

1. Knowledge Discovery

2. Hypothesis Generation

BioAgents can generate testable hypotheses based on knowledge graph patterns:

• Identify gaps in the knowledge graph
• Apply reasoning to suggest potential connections
• Rank hypotheses by plausibility
• Provide supporting evidence

3. Data Integration

The knowledge graph connects data across multiple sources:

• Research papers
• Experimental results
• External databases
• Clinical data
• Genomic information