rahulnyk/knowledge_graph
Knowledge Graph
This project is a tool for transforming unstructured text into semantic knowledge graphs. It uses local language models to extract entities and their relationships, converting text corpora into a structured network of linked concepts.
The system provides a web interface for interactive network visualization, allowing users to navigate the resulting nodes and edges. It includes a topology analysis tool that calculates node degrees and identifies community clusters to determine the visual size and color of graph elements.
Beyond visualization, the project enables graph-based information retrieval. This allows for the location of specific data by traversing semantic connections rather than relying on keyword searches.
Features
- Knowledge Graph Extraction - Transforms unstructured text into structured knowledge representations by identifying entities and their semantic relationships.
- Entity Extraction Pipelines - Provides an automated pipeline that uses local language models to parse raw text into structured nodes and edges.
- Knowledge Graph Construction - Automates the build process of graph structures from unstructured text datasets.
- Local Model Inference Servers - Hosts language models locally to process documents while ensuring data privacy and minimizing latency.
- Text-to-Graph Transformers - Converts unstructured text corpora into structured property graphs using local language models.
- Knowledge Graph Builders - Builds structured knowledge graphs from raw text by extracting organized facts and triples.
- Semantic Networks - Implements a graph-based memory structure using nodes and edges to store concepts and their semantic relationships.
- Graph Node Clustering - Groups graph nodes into clusters based on structural similarity to understand community distribution.
- Graph-Based Retrieval Engines - Uses a knowledge graph engine to improve the accuracy of information retrieval from unstructured documents.
- Semantic Topology Mapping - Calculates node degrees and identifies community clusters to determine the size and color of visual graph elements.
- Semantic Traversals - Enables information retrieval by traversing semantic connections between entities instead of relying on keyword searches.
- Graph Topology Analysis - Identifies key clusters and influential nodes by calculating centrality metrics to understand data distribution.
- Graph-Based Retrieval - Finds specific information within a dataset by traversing a network of related nodes.
- Interactive Graph Visualizers - Renders interactive directed graphs in a browser environment for analyzing complex data relationships.
- Interactive Visualization Rendering - Provides an interactive web interface for rendering dynamic semantic networks that update based on user navigation.
- Semantic Graph Analyzers - Calculates node degrees and performs community detection to determine the visual properties of network elements.
