HighlightNeo4j

Neo4j graph modeling ties domain questions to storage shape

Neo4j getting-started overview of graph data modeling steps from domain use cases through test, Cypher load, and refactor.

Graph data modeling defines query logic and stored structure; a well-designed model improves performance, flexibility, and storage use. The process covers understanding the domain and use-case questions, extracting entities and relationships, testing against an initial model, loading test data with Cypher, measuring performance, and refactoring as use cases change.

Based on: What is graph data modeling? - Getting Started · Neo4j

HighlightNeo4j

Neo4j constraints enforce uniqueness, existence, type, and keys

Neo4j Cypher manual listing property uniqueness, existence, type, and key constraints, and preferring graph types for schema.

Neo4j provides property uniqueness, existence (Enterprise), type (Enterprise), and key (Enterprise) constraints on nodes by label or relationships by type. Older CREATE CONSTRAINT syntax still adds constraints to a database’s graph type, but the docs recommend defining schema via graph types for richer constraint kinds and simpler long-term maintenance.

Based on: Constraints - Cypher Manual · Neo4j

HighlightNeo4j

Neo4j MERGE: match-or-create patterns with ON MATCH/ON CREATE

Neo4j Cypher manual for MERGE, which binds existing patterns or creates missing ones, with constraint guidance.

MERGE combines MATCH and CREATE: if the exact pattern exists it binds like MATCH; otherwise it creates like CREATE. ON MATCH and ON CREATE allow different actions for each case. The manual recommends creating constraints before merging for index-backed performance and to prevent unintended divergent data.

Based on: MERGE - Cypher Manual · Neo4j

HighlightApache Software Foundation

Apache Jena’s jena-shacl runs W3C SHACL Core and SPARQL constraints in practice

Jena documentation for jena-shacl: SHACL Core and SPARQL constraints, compact syntax, CLI validate/parse, and Fuseki endpoint integration.

jena-shacl implements W3C SHACL Core and SHACL SPARQL Constraints, plus compact syntax and SPARQL-based targets. The shacl CLI validates and parses shapes; Fuseki can expose a fuseki:shacl operation that accepts a posted shapes graph and optional graph/target parameters.

Based on: Apache Jena - Apache Jena SHACL · Apache Software Foundation

Highlightgit.nextgraph.org

oxigraph

A Rust-based graph database implementing the SPARQL standard.

Oxigraph is a graph database written in Rust that implements the SPARQL standard. It provides a compliant, safe, and fast graph database based on the RocksDB key-value store. Oxigraph also includes utility functions for reading, writing, and processing RDF files.

Based on: oxigraph · git.nextgraph.org

Highlightgithub.com

DuckDB RDF Extension

A DuckDB extension to read and write RDF files directly.

This extension allows reading and writing RDF files in DuckDB, supporting various formats such as Turtle, NTriples, NQuads, and TriG. It uses the SERD library for parsing and writing RDF data. The extension also supports WebAssembly (WASM) builds and compression formats like Gzip and Zst.

Based on: GitHub - nonodename/duck_rdf: RDF file extension for DuckDB. Reads and writes supported · github.com

HighlightProceedings of the ACM on Management of Data

PG-Schema: Schemas for Property Graphs

A formalism for specifying property graph schemas with flexible type definitions and expressive constraints.

The authors propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features flexible type definitions supporting multi-inheritance and expressive constraints based on the recently proposed PG-Keys formalism. The paper provides the formal syntax and semantics of PG-Schema, meeting principled design requirements grounded in contemporary property graph management scenarios.

Based on: PG-Schema: Schemas for Property Graphs · Proceedings of the ACM on Management of Data

HighlightVLDB Endowment

QSE: Extraction of Validating Shapes from Very Large Knowledge Graphs

Proposes a Quality Shapes Extraction approach for extracting validating shapes in very large knowledge graphs.

The paper presents the QSE approach, which uses SHACL/ShEx to extract validating shapes from large knowledge graphs. It provides both exact and approximate solutions with confidence metrics. The authors achieve significant speed improvements and spurious shape reductions on DBpedia.

Based on: QSE: Extraction of Validating Shapes from Very Large Knowledge Graphs · VLDB Endowment