Enliterator Research Platform
A Rails 8 platform for creating graph-constrained collaborators whose inference is bounded by structured knowledge graphs, demonstrating systematic realization of Apple's 1987 Enliterated Knowledge Navigator concept through rights-aware knowledge representation and fine-tuned language models.
Research Milestone: First Enliterated Knowledge Navigator Implementation
Arctic Navigator demonstrates complete implementation on August 13, 2025, validating the methodology against established Enliterated Knowledge Navigator criteria through comprehensive evaluation metrics.
Research Methodology
This platform addresses the challenge of creating conversational AI systems that demonstrate domain expertise rather than general-purpose question-answering capabilities.
Key technical contributions include:
- Domain specialization through fine-tuned language models
- Knowledge grounding via structured graph representations
- Consistent communication patterns through personality calibration
- Provenance tracking with comprehensive citation systems
- Adaptive interaction based on user context and expertise
Implementation Validation: Arctic Navigator
Arctic Navigator implementation data not available
Advanced Architecture: MCP-Enabled Nested EKNs
Graph-Constrained Collaboration
- β’ EKNs expose their knowledge graphs via Model Context Protocol (MCP)
- β’ Other EKNs can access and query external graphs when permitted
- β’ Creates nested architectures for complex, multi-domain knowledge
- β’ Maintains graph-bounded inference across the collaboration
Collaborative Intelligence
This architecture enables sophisticated multi-domain reasoning while preserving the reliability of graph-bounded inference across the entire collaborative network.
Implementation Architecture: 13-Stage Pipeline
Knowledge Infrastructure
Data Processing & Graph Construction
- β’ Domain initialization and configuration
- β’ Data ingestion and preprocessing
- β’ Rights assignment and provenance tracking
- β’ Lexicon extraction and canonicalization
- β’ Entity extraction using 15-pool taxonomy
- β’ Neo4j knowledge graph assembly
- β’ Multi-pass relationship discovery
- β’ Vector embedding generation
- β’ Knowledge completeness validation
Conversational Intelligence
Model Specialization & Calibration
- β’ Domain-specific model fine-tuning
- β’ Conversational dataset generation
- β’ Communication pattern calibration
- β’ Response consistency optimization
- β’ Domain expertise validation
System Integration
Validation & Deployment
- β’ Component integration testing
- β’ Performance benchmarking
- β’ Enliterated Knowledge Navigator criteria validation
- β’ Production deployment assessment
- β’ System certification
Technical Architecture
Core Components
- QueryOrchestrator: Intelligent query routing using fine-tuned models for canonical term mapping and tool selection
- Knowledge Grounding: Response generation constrained by structured graph entities with comprehensive provenance tracking
- Communication Calibration: Statistical analysis of successful interaction patterns for consistent domain-specific communication
- Rights Management: Query-time filtering based on data permissions and usage constraints
Implementation Stack
- Rails 8: Web application framework with Solid Queue background processing
- Neo4j + GenAI: Graph database with integrated vector embeddings for semantic search
- OpenAI GPT-4.1-mini: Base model with domain-specific fine-tuning for canonical understanding
- MCP Protocol: Model Context Protocol implementation for structured tool interaction
Universal Knowledge Architecture: The 10+5 Pool System
A universal ontological framework designed to represent domain knowledge through a standardized 15-entity taxonomy that maintains semantic clarity while enabling computational reasoning across diverse knowledge domains.
Ten Pool Canon: Core Entity Types
Foundational entity categories designed to systematically represent knowledge across domains, spanning conceptual abstractions, physical manifestations, experiential data, and empirical measurements.
Ideas
Concepts, principles, theories - the why behind everything
Manifests
Physical objects, structures, locations - the tangible what
Experiences
Events, activities, interactions - lived outcomes and perceptions
Actors
People, organizations, roles - the who in every story
Evidence
Data, measurements, observations - the proof and validation
Spatial
Geographic, coordinate information - the where of knowledge
Practical
Procedures, methods, workflows - the actionable how
Evolutionary
Changes, developments over time - the when and progression
Risks
Hazards, challenges, concerns - anticipating what could go wrong
Methods
Approaches, techniques, tools - the systematic ways of doing
Extended Pools: Advanced Capabilities
Five additional specialized pools that enable advanced features like rights management, relationship discovery, and semantic understanding.
Lexicon & Ontology
Terminology, definitions - canonical language understanding
Provenance & Rights
Source tracking, permissions - ethical data usage
Relational
Connections, networks - how everything links together
Intent & Task
Goals, objectives, actions - user requests and fulfillment
Emanation
Outputs, ripple effects - influence and consequences
Computational Capabilities Enabled by Universal Architecture
Knowledge Representation
- Semantic Disambiguation: Formal entity typing eliminates ambiguity in knowledge graph structure
- Query Precision: Fine-tuned models leverage pool taxonomy for accurate information retrieval
- Relationship Inference: Structured entity types enable systematic relationship discovery across knowledge domains
- Temporal Modeling: Evolutionary pool captures change patterns and temporal dependencies
System Implementation
- Verified Attribution: Comprehensive provenance tracking with entity-level source citation
- Rights Enforcement: Query-time filtering based on usage permissions and data classification
- Spatial Computing: Geographic entity relationships enable location-aware reasoning
- Risk Analysis: Systematic hazard identification through risk pool integration
Cross-Domain Applicability
The 15-pool taxonomy demonstrates domain-agnostic knowledge representation capabilities. Validation across Arctic research, with planned extension to medical, legal, and engineering domains, supports the hypothesis that this framework can systematically represent knowledge across diverse fields.
Research Contribution
Technical Achievements and Validation
- β’ Systematic implementation of Apple's 1987 Enliterated Knowledge Navigator concept using modern LLM technology
- β’ Domain-specific conversational AI that demonstrates verifiable expertise through structured knowledge grounding
- β’ Reproducible methodology for creating domain-specific AI systems across knowledge fields
- β’ Integration of knowledge graphs with fine-tuned language models for reliable, citation-backed responses
This work demonstrates a methodology for transitioning from general-purpose conversational AI to domain-specific knowledge systems that maintain expert-level consistency and verifiability.
Research Implementation Access
Implementation details available through the GitHub repository. System architecture combines Rails 8, Neo4j knowledge graphs, and OpenAI GPT-4.1-mini to demonstrate domain-specific conversational AI capabilities.