OpenSPG is an open-source knowledge graph engine designed for complex domain modeling, fact and logic fusion, and enhanced LLM integration. It targets enterprises and researchers needing robust, scalable knowledge graph solutions, offering a programmable framework for efficient data-to-knowledge transformation and AI application development.

How It Works

OpenSPG is built on the Semantic-enhanced Programmable Graph (SPG) framework, which merges the structural simplicity of Property Graphs with the semantic richness of RDF. This hybrid approach aims to overcome the industrial adoption challenges of complex semantic web standards while retaining the benefits of property graphs for big data systems. SPG explicitly defines knowledge, supports iterative evolution of incomplete data, and bridges big data and AI systems.

Quick Start & Requirements

• Installation and usage details are available via quick-start examples and a user guide.
• Specific hardware or software prerequisites are not detailed in the README.

Highlighted Details

• SPG-Schema: Enables semantic enhancement of property graphs with subject, evolutionary, and predicate models.
• SPG-Builder: Supports structured and unstructured knowledge construction, integrating with big data architectures and offering NLP/deep learning operators for entity linking and normalization.
• SPG-Reasoner: Abstracts KGDSL for programmable symbolic logic rules, supporting inference, hybrid learning, and KG2Prompt for LLM integration.
• KNext: A programmable framework abstracting core KG capabilities into components, isolating engine from business logic for rapid solution development.

Maintenance & Community

The project is a collaboration between Ant Group and OpenKG, with a core team listed. Contribution guidelines are provided.

Licensing & Compatibility

• License: Apache License 2.0.
• Compatibility: Permissive license suitable for commercial use and integration with closed-source systems.

Limitations & Caveats

The README does not detail specific system requirements, performance benchmarks, or known limitations, requiring users to explore the provided examples and guides for practical assessment.