RAG and Knowledge Engineering
Knowledge Graph — Stack
Knowledge graph › Stack
Technology landscape as of 2026-10-10. Knowledge cutoff May 2026.
Selection criteria
Three separable components — a store, an extraction pipeline, and an identity resolution mechanism — plus the question of how much formal semantics you actually want.
- Schema enforcement. Whether the store can reject an assertion that violates the model, or whether validation is an external batch job over data already written.
- Provenance at edge granularity. Whether an edge can carry its source document, span and extraction run without modelling contortions such as reification of every statement.
- Temporal modelling. Native support for validity intervals and assertion time, or the absence of it, which determines whether "was true then" is expressible.
- Identity and merge mechanics. Whether merges are first-class and reversible, or implemented as destructive rewrites.
- Query ergonomics for traversal — variable-length paths, degree-aware planning, and whether the query language can express the patterns your questions need.
- Write throughput for bulk extraction loads, which differ sharply from the interactive read pattern the store is usually benchmarked on.
- Rebuild and migration story. How a schema change or a re-extraction is applied to an existing graph without losing curated content.
- Interoperability with the structured systems whose identifiers the graph should be reusing rather than minting its own.
Options
| Option | Best for | Watch out for |
|---|---|---|
| Property-graph databases (Neo4j, Memgraph, TigerGraph, Amazon Neptune) | Traversal-heavy use, pragmatic modelling and mature tooling | No native schema enforcement in most; provenance and temporality are modelling conventions you must enforce |
| RDF triple stores with OWL or SHACL (Apache Jena, GraphDB, Stardog, Virtuoso) | Formal semantics, validation, inference and interoperable identifiers | Statement-level provenance requires reification or named graphs; the modelling investment is substantial |
| Relational schemas with explicit entity and relation tables | Graphs with few relation types, heavy attribute use and existing SQL infrastructure | Variable-length traversal in SQL is painful; the limit arrives suddenly |
| Datalog and deductive engines (Datomic, XTDB, RDFox) | Rule-driven derivation and bitemporal history as a built-in property | Smaller ecosystems; operational knowledge is scarcer |
| Model-based extraction pipelines (instructed LLM extraction, LangChain graph transformers, LlamaIndex property-graph construction) | Getting assertions out of prose without annotated training data | Output varies between runs and model versions; constrain to a schema and validate before writing |
| Trained extraction models (spaCy pipelines, GLiNER-style zero-shot taggers, fine-tuned sequence labellers) | Stable, high-volume extraction of a fixed set of types | Needs labelled data or careful prompt-free configuration; narrower coverage than a general model |
| Entity resolution systems (Splink, Zingg, dedupe, commercial master-data tooling) | Deciding identity at scale with measurable precision and recall | Needs blocking keys and labelled pairs; a resolution threshold is a product decision, not a default |