meqkg

meqkg, short for Minimum Equivalent Quality Knowledge Graph, is an algorithmic framework for creating compact representations of knowledge graphs that preserve semantic integrity while minimizing storage and computational overhead. The concept was first introduced in a 2021 research paper by Dr. Lina Patel and colleagues at the Institute for Advanced Data Systems, who sought to address the rapidly increasing size of enterprise knowledge bases. They demonstrated that by applying a series of graph pruning and edge weighting techniques, it is possible to reduce graph size by up to 60 % without significant loss of query performance or inferencing capability.

The meqkg framework operates in three stages: normalization of node attributes, calculation of semantic similarity scores, and iterative edge removal guided by a quality metric that balances structural fidelity against compactness. The resulting minimized graph can be stored in traditional triple-store databases or encoded as compact array structures for in-memory analytics. Subsequent work has extended the method to dynamic environments, enabling incremental updates that preserve the minimum‑quality guarantee.

Real‑world implementations have appeared in several commercial knowledge management products, including the open‑source platform NeoMiner and the proprietary system CogniGraph by MetaData Corp. Critics argue that aggressive pruning may inadvertently discard less frequently traversed but semantically important paths, potentially impacting inferencing in niche domains. Nevertheless, the meqkg approach remains a widely cited reference point in research on efficient knowledge graph storage and query optimization.