knowledge-graph

Choose RDF when interoperability, shared vocabularies, web identifiers, and formal OWL reasoning matter. Choose a labeled property graph when you need rich attributes on edges, high-performance traversals, and graph analytics on operational data.

SPARQL matches triple patterns against RDF data with OPTIONAL, UNION, FILTER, and property paths. Cypher uses ASCII-art patterns like (a)-[:KNOWS]->(b) with MATCH, WHERE, MERGE, and variable-length paths for property graphs.

Entity resolution deduplicates records referring to the same real-world entity through blocking, similarity matching, clustering, and assigning canonical identifiers. It is the step that most determines overall graph quality.

Yes, the Neo4j Graph Data Science library provides node embeddings such as Node2Vec, FastRP, and GraphSAGE, plus link prediction and node classification. It also includes centrality, community detection, and pathfinding algorithms.

GraphRAG grounds LLM answers by retrieving a relevant subgraph via entity linking and traversal, optionally combined with vector search. It outperforms plain vector RAG for multi-hop reasoning, explicit relationships, and explainable provenance.

Avoid a graph when a relational table with a join answers the question, since graphs shine on many-hop, relationship-centric queries. Also avoid unbounded variable-length traversals on cyclic graphs, which can explode in cost.