# rahulnyk/knowledge_graph

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## Links

- GitHub: https://github.com/rahulnyk/knowledge_graph
- awesome-repositories: https://awesome-repositories.com/repository/rahulnyk-knowledge-graph.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Knowledge Graph Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/knowledge-graph-extraction.md) — Transforms unstructured text into structured knowledge representations by identifying entities and their semantic relationships. ([source](https://cdn.jsdelivr.net/gh/rahulnyk/knowledge_graph@main/README.md))
- [Entity Extraction Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/entity-extraction-pipelines.md) — Provides an automated pipeline that uses local language models to parse raw text into structured nodes and edges.
- [Knowledge Graph Construction](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/knowledge-graph-engineering/knowledge-graph-construction.md) — Automates the build process of graph structures from unstructured text datasets.
- [Local Model Inference Servers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/local-and-on-device-inference/local-model-inference-servers.md) — Hosts language models locally to process documents while ensuring data privacy and minimizing latency.
- [Text-to-Graph Transformers](https://awesome-repositories.com/f/artificial-intelligence-ml/text-to-graph-transformers.md) — Converts unstructured text corpora into structured property graphs using local language models. ([source](https://rahulnyk.github.io/knowledge_graph/))
- [Graph Node Clustering](https://awesome-repositories.com/f/artificial-intelligence-ml/clustering-algorithms/spatial-clustering/graph-node-clustering.md) — Groups graph nodes into clusters based on structural similarity to understand community distribution.
- [Graph-Based Retrieval Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/graph-based-retrieval-engines.md) — Uses a knowledge graph engine to improve the accuracy of information retrieval from unstructured documents. ([source](https://rahulnyk.github.io/knowledge_graph/))

### Data & Databases

- [Knowledge Graph Builders](https://awesome-repositories.com/f/data-databases/knowledge-graph-indexers/knowledge-graph-builders.md) — Builds structured knowledge graphs from raw text by extracting organized facts and triples.
- [Semantic Topology Mapping](https://awesome-repositories.com/f/data-databases/cluster-topology-visualization/semantic-topology-mapping.md) — Calculates node degrees and identifies community clusters to determine the size and color of visual graph elements.
- [Semantic Traversals](https://awesome-repositories.com/f/data-databases/entity-relationships/semantic-traversals.md) — Enables information retrieval by traversing semantic connections between entities instead of relying on keyword searches.
- [Graph Topology Analysis](https://awesome-repositories.com/f/data-databases/graph-topology-analysis.md) — Identifies key clusters and influential nodes by calculating centrality metrics to understand data distribution. ([source](https://cdn.jsdelivr.net/gh/rahulnyk/knowledge_graph@main/README.md))
- [Graph-Based Retrieval](https://awesome-repositories.com/f/data-databases/information-retrieval/graph-based-retrieval.md) — Finds specific information within a dataset by traversing a network of related nodes.
- [Interactive Graph Visualizers](https://awesome-repositories.com/f/data-databases/interactive-graph-visualizers.md) — Renders interactive directed graphs in a browser environment for analyzing complex data relationships. ([source](https://cdn.jsdelivr.net/gh/rahulnyk/knowledge_graph@main/README.md))
- [Interactive Visualization Rendering](https://awesome-repositories.com/f/data-databases/interactive-visualization-rendering.md) — Provides an interactive web interface for rendering dynamic semantic networks that update based on user navigation.

### Software Engineering & Architecture

- [Semantic Networks](https://awesome-repositories.com/f/software-engineering-architecture/shared-memory-management/shared-knowledge-graph-memory/semantic-networks.md) — Implements a graph-based memory structure using nodes and edges to store concepts and their semantic relationships.

### Development Tools & Productivity

- [Semantic Graph Analyzers](https://awesome-repositories.com/f/development-tools-productivity/dependency-analysis-tools/code-dependency-analysis/static-dependency-resolution/graph-analysis-tools/semantic-graph-analyzers.md) — Calculates node degrees and performs community detection to determine the visual properties of network elements.