🛰️ Sat-KG-Pipeline

Sat-KG-Pipeline is an intelligent system that transforms natural language troubleshooting inputs into a structured Knowledge Graph to assist satellite engineers in diagnosing faults in power systems. By integrating AI language models and semantic technologies, this pipeline enables automated fault reasoning by mapping ambiguous or high-level descriptions into machine-readable fault scenarios.

🤖 NLP-Driven Fault Understanding

Uses co-reference resolution, tokenization, and chunk-based processing to extract key information from user inputs, transforming natural language troubleshooting descriptions into structured data.

🕸️ Knowledge Graph Construction

Converts extracted insights into RDF triples and stores them in a SPARQL-compatible triplestore for efficient querying and fault pattern analysis.

📚 Real-Time Information Augmentation

Leverages SerpAPI and OCR to enrich context from technical documents and online sources, providing comprehensive fault diagnosis support.

⚡ Designed for Satellite Power Systems

Specifically tailored to handle diagnostic patterns and failure modes relevant to satellite electrical subsystems, ensuring domain-specific accuracy.

Languages & Frameworks

Libraries & APIs

Techniques

1. Input Processing

Engineers provide troubleshooting inputs in natural language describing observed faults or anomalies in satellite power systems.

2. NLP Analysis

The system applies advanced NLP techniques including co-reference resolution and tokenization to extract key entities, relationships, and fault indicators from the input text.

3. Knowledge Graph Construction

Extracted information is converted into RDF triples, creating a structured knowledge graph that represents fault scenarios, components, and their relationships.

4. Information Enrichment

The pipeline augments the knowledge graph with additional context from technical documentation and online sources using SerpAPI and OCR capabilities.

5. SPARQL Querying

Engineers can query the knowledge graph using SPARQL to identify similar fault patterns, retrieve diagnostic procedures, and access relevant troubleshooting information.

🛰️ This pipeline bridges the gap between human expertise and machine reasoning, enabling faster and more accurate fault diagnosis in satellite power systems. By translating basic troubleshooting inputs into a structured, queryable knowledge base, satellite engineers can leverage collective knowledge and automated reasoning to maintain critical space infrastructure.

🔧 The system represents a significant advancement in applying AI and semantic web technologies to aerospace engineering challenges, demonstrating the power of knowledge graphs in specialized technical domains.