mkgs

mkgs, short for Multi-Kernel Graph System, is a modular, open‑source framework for building and maintaining knowledge graphs and for generating structured content from them. It combines a graph data store, multiple processing kernels for ingestion, reasoning, and content synthesis, and an API layer that enables integration with applications and AI models. The design emphasizes interoperability, extensibility, and provenance, supporting entities, relationships, and rules across multilingual data.

At its core mkgs provides data ingestion pipelines, a graph database backend, a reasoning engine, and a content generation module that can produce summaries, reports, or structured outputs. It supports data interchange formats such as RDF, JSON-LD, and CSV, and interfaces through REST, GraphQL, and SDKs. The architecture is built around modular kernels that can be swapped or extended without affecting the overall graph.

Development is community‑driven, with contributions from researchers and practitioners. The project prioritizes standards conformance, portability across storage backends, and efficient traversal for large graphs. It emphasizes data lineage, access control, and versioning to support governance in organizational environments.

Common use cases include knowledge management and discovery, data integration in enterprises, research data curation, and AI‑assisted content creation. By combining graph semantics with generation capabilities, mkgs aims to provide coherent narratives and structured outputs while preserving source provenance.

Limitations include the complexity of setup, a learning curve for kernel configuration, and performance considerations for very large graphs. Adoption benefits depend on clear schemas, governance policies, and active community support.

Related topics include knowledge graphs, graph databases, SPARQL, RDF, JSON‑LD, and generative AI.