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see Pangea for a concrete example

System Description

Overview

The system enables semi-automatic ontology generation and federated querying across diverse data sources, based on natural language use case descriptions. It builds upon Tim Holzheim's master thesis approach of faceted search to create dynamic, context-aware models.

Input

Natural language use case description (4-5 sentences)
Reference to a knowledge graph
Starting item/entry point
Connection to multiple data sources with varying formats and structures

Supported Data Sources

Tabular Data

Excel spreadsheets
CSV (Comma-Separated Values)
TSV (Tab-Separated Values)
SQL databases

Hierarchical Data

JSON
XML
File systems
Microsoft Office documents (PowerPoint, Word)

Graph Data

RDF/SPARQL endpoints
Property Graphs
GraphQL APIs
Neo4j/Cypher

Other Sources

REST APIs
Web services
Custom data formats
HTML crawling

Core Functionality

Model Generation

Dynamic ontology creation based on use case context
(Semi-) Automatic mapping of concepts to existing knowledge graph entities
Integration of Object-Oriented Analysis (OOA) principles
Validation of model consistency and completeness

Query Generation

Automatic creation of parameterized queries
Translation between different query languages
Query optimization for federated execution
Support for multiple technical representations

Data Integration

Mapping between different data models
Object-Oriented Design (OOD) pattern application
Schema alignment and reconciliation
Identity resolution across sources

Technical Implementation

Architecture Components

Natural Language Processing (NLP) module
Model generation engine
Query translation layer
Federation middleware
Data source connectors

Design Considerations

Pragmatic compromises for end-to-end functionality
Balance between model expressiveness and query performance
Scalability across diverse data sources
Maintainability of generated artifacts

Object-Oriented Integration

Implementation of Object-Oriented Implementation (OOI) patterns
Mapping between object-oriented and graph models
Class and property inheritance handling
Instance management across systems

Constraints and Limitations

Performance implications of federated queries
Complexity of maintaining consistency across diverse sources
Trade-offs between automation and accuracy
Technical limitations of different data sources

Future Extensions

Additional data source support
Enhanced natural language understanding
Improved query optimization
Extended model validation capabilities

KG Explorer

Contents