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Knowledge Graph

The HealthyPhases Knowledge Graph is a semantically-rich, interconnected representation of solitude and gerotranscendence research findings, concepts, and data.

What is a Knowledge Graph?​

A knowledge graph is a structured representation of knowledge where:

  • Entities (people, concepts, studies, measures) are represented as nodes
  • Relationships between entities are represented as labeled connections
  • Properties provide attributes and details about the entities
  • Context gives meaning and provenance to the information

Unlike traditional databases, knowledge graphs excel at representing complex, interconnected information from diverse sources.

The HealthyPhases Knowledge Graph​

Our knowledge graph integrates multiple types of information:

  • Research Findings: Results from published studies
  • Concepts and Definitions: Terminology and formal definitions
  • Measurement Instruments: Structure and use of assessment tools
  • Expert Knowledge: Insights from domain specialists
  • Data Patterns: Derived trends and relationships

Key Features​

Interconnected Structure​

The knowledge graph connects related concepts across traditional boundaries:

  • Links between psychological, social, and physical aspects of aging
  • Connections between subjective experiences and objective measures
  • Integration of findings across disciplines and methodologies

Semantic Foundation​

Built on formal ontological principles:

  • Aligned with the Basic Formal Ontology (BFO)
  • Incorporates terms from multiple domain ontologies
  • Provides precise definitions for all concepts
  • Supports logical inference and reasoning

Flexible Exploration​

The graph can be explored from multiple perspectives:

  • By concept (e.g., "voluntary solitude")
  • By study or publication
  • By measurement approach
  • By population characteristics
  • By outcomes and correlates

Applications​

For Researchers​

  • Hypothesis Generation: Discover potential relationships to investigate
  • Literature Exploration: Find relevant research across disciplinary boundaries
  • Research Context: Understand how your work fits into the broader knowledge landscape
  • Gap Analysis: Identify understudied areas or relationships

For Practitioners​

  • Evidence Summary: Access integrated evidence on specific questions
  • Intervention Planning: Identify factors that influence outcomes
  • Assessment Selection: Find appropriate measures for specific contexts
  • Client Education: Visualize relationships for client discussions

For Data Scientists​

  • Data Integration: Connect your datasets to a broader knowledge context
  • Enrichment Source: Use the graph to enrich your data
  • Feature Identification: Discover potential variables for models
  • Pattern Validation: Compare discovered patterns with known relationships

Accessing the Knowledge Graph​

Visual Explorer​

Our web-based interface allows visual exploration of the graph through:

  • Interactive network visualizations
  • Guided exploration paths
  • Filtering by entity types and relationships
  • Custom visualizations for specific questions

Query Interface​

For more technical users, we provide:

  • SPARQL endpoint for custom queries
  • GraphQL API for application integration
  • Downloadable subgraphs for specific topics
  • Query templates for common research questions

Technical Implementation​

The HealthyPhases Knowledge Graph is implemented using:

  • RDF/OWL for semantic representation
  • Named graphs for tracking provenance
  • SHACL for validation and quality control
  • Triple store technology for storage and querying

Contributing​

The knowledge graph is continuously expanded through:

  • Automated extraction from publications
  • Contributed datasets
  • Expert curation
  • User feedback and suggestions

Resources​

Contact​

For questions about using or contributing to the Knowledge Graph, please contact [email protected].