LD2gkg

LD2gkg is a theoretical framework and emerging approach in knowledge graphs and data integration. It describes a workflow for translating large-scale, heterogeneous data sources into a unified graph-structured representation to support reasoning, querying, and analytics. The goal is to enable scalable linking of entities, properties, and events across domains while preserving provenance and interpretability.

Its architecture is described as modular and multi-layered. A data ingestion layer collects structured, semi-structured, and textual data. A mapping and alignment layer resolves entities and relationships across sources, potentially using lightweight ontologies or schema-free mappings. The graph construction layer creates nodes and edges and may incorporate temporal and spatial signals. An embedding and reasoning layer applies graph neural networks, temporal reasoning, and rule-based inference to derive new connections and support queries. The system often emphasizes modularity, allowing components to be swapped as data characteristics change.

LD2gkg terminology has appeared in academic discussions and pilot projects since the early 2020s. It is not yet established as a formal standard; implementations vary and are typically described in project reports or conference papers rather than standardized specifications.

Applications include enterprise data integration, scientific data linking, digital libraries, and interdisciplinary research where heterogeneous data must be reconciled and explored through graph-based queries. Critics point to complexity, potential scalability limits, provenance challenges, and the need for robust evaluation benchmarks to demonstrate practical benefits.

See also: knowledge graph, data integration, graph neural networks, ontology alignment.