MySQL-backed, limited by PHP memory. Suitable for small datasets only
Slow by modern standards. PHP + MySQL overhead. Largely superseded by other tools
PHP library for working with RDF data including parsing, serializing, storing, and SPARQL querying
Handles billions of triples. Multi-master replication for horizontal scaling
Solid performance especially for complex reasoning queries. Geospatial and temporal indexing are fast
High-performance persistent graph database by Franz Inc. with RDF, SPARQL, RDFS++, Prolog reasoning, and federated queries
Pay-per-use (from ~$0.348/instance-hour)
Scales via AWS infrastructure. Serverless option auto-scales. No upper limit on data size
Managed service - performance depends on instance size. Competitive but not the fastest per dollar
Fully managed AWS graph database supporting RDF/SPARQL and property graphs with TinkerPop/Gremlin and openCypher
Free Edition (8-16 GB) + Commercial Enterprise
In-memory architecture. Free edition limited to 16GB. Scales via distributed memory in enterprise
Very fast for analytical/aggregation queries - designed for OLAP-style workloads on graph data
In-memory analytical graph database (originally by Cambridge Semantics, now part of Altair/Siemens) supporting RDF/SPARQL with high-speed analytics on massive datasets. Also known as Altair Graph Studio.
No storage - delegates to backing implementation
API layer only - performance depends entirely on the backing implementation (Jena, RDF4J, etc.)
Common Java interfaces for RDF 1.1 providing a type-safe API with implementations for Jena, RDF4J, and JSON-LD. Effectively dormant since 2017, though not formally retired.
Mature and proven at scale with TDB2. Handles hundreds of millions of triples in production
Solid Java performance for I/O and queries. JVM warmup cost but good sustained throughput
Comprehensive Java framework for building Semantic Web and Linked Data applications, with RDF API, SPARQL engine, and OWL reasoning
Production-proven with TDB2. Handles hundreds of millions of triples
Same engine as Jena, with HTTP overhead. TDB2 backend gives good persistent storage performance
SPARQL server providing RDF data over HTTP with SPARQL 1.1 query/update protocols, integrated with Jena TDB persistent store
Scales with the backing triple store. Cloud option available for managed deployments
Java-based platform - performance depends on backing SPARQL store
Low-code Knowledge Graph application platform built entirely on RDF and SPARQL. Supports faceted search, set-based navigation, and declarative data-driven web apps with LDP and DCAT support
Handles billions of triples (proven by Wikidata). Unmaintained since 2020 - no future improvements
Good performance - powers Wikidata Query Service. Optimized for read-heavy workloads
High-performance graph database supporting RDF/SPARQL; powers the Wikidata Query Service. No longer actively maintained (archived 2026).
Designed for federated querying across many small sources, not for querying a single massive dataset
Moderate - JS/TS overhead vs. native engines. Strength is querying heterogeneous sources, not raw speed
Highly modular meta SPARQL query engine for the Web, querying over heterogeneous Linked Data interfaces
Designed for consuming small amounts of Linked Data in web apps, not bulk processing
Adequate for PHP web applications. Not performance-oriented
PHP library designed to make it easy to consume and produce RDF, with support for multiple serialization formats
Production-grade. LMDB native store handles large datasets well. Used by GraphDB under the hood
Comparable to Jena for most operations. LMDB store mode improves disk performance significantly
Eclipse framework for processing and handling RDF data, with parsers, triple stores (memory, native, LMDB), SPARQL query engine, and reasoning
Enterprise-grade. Handles billions of triples in cluster mode. Free edition limited to single-node
Moderate - reasoning overhead slows it vs. pure stores. Good for enterprise workloads with inference
Enterprise RDF graph database by Ontotext with reasoning, SPARQL support, and a visual workbench; free edition available with limited concurrency
Gruff AllegroGraph Franz graph visualization
Visualizes subsets of larger graphs - not designed to render millions of nodes
Desktop visualization tool - performance tied to AllegroGraph backend
Visual RDF graph browser and query builder by Franz Inc. for exploring and visualizing knowledge graphs stored in AllegroGraph
Handles billions of triples in compressed form. Read-only - no updates without rebuilding
Extremely fast triple-pattern lookups on compressed data. 5-10x smaller than raw Turtle/NT
Compact binary serialization format for RDF that supports efficient storage and triple-pattern querying without decompression
In-memory - performs well on complex ontologies but bounded by available RAM
Often faster than Pellet for classification. Hypertableau algorithm handles complex ontologies well
OWL 2 reasoner based on hypertableau calculus with support for classification, DL-safe rules, and SPARQL queries
Browser-based - instant processing for individual JSON-LD documents
Interactive online tool for testing JSON-LD documents with expansion, compaction, flattening, framing, and RDF conversion
Early-stage project. Performance is promising but production-readiness is still developing
Very fast I/O and queries - Rust-based. Among the quickest for parsing and simple SPARQL
SPARQL database, RDF Stream Processing engine, and RDF toolkit with rule-based inference and backward chaining from KU Leuven
Designed for scalable publishing - minimal server load even with many concurrent clients
Server is lightweight by design - pushes query work to the client. Low server resource usage
Server providing Triple Pattern Fragments for low-cost, scalable Linked Data publishing that distributes query load to clients
Free Developer Edition + Commercial (from ~$20K)
Enterprise-grade clustering. Handles very large datasets across distributed nodes
Optimized for mixed document/RDF workloads. SPARQL performance is adequate but not its primary strength
Enterprise NoSQL multi-model database natively storing JSON, XML, and RDF triples with SPARQL, full-text search, and AI features
Designed for writing and testing mappings, not production execution
Browser-based editor - instant preview for small mappings
Web-based editor for writing YARRRML mapping rules with live RDF preview and export to RML
Handles Wikidata-scale datasets. Persistent disk-based storage
Fast query processing through worst-case optimal joins. Competitive on Wikidata-scale workloads
Open-source graph database from the Millennium Institute (IMFD, Chile) supporting both RDF/SPARQL and property graphs, with worst-case optimal join algorithms
Handles large mapping jobs well. Designed for ETL pipelines, not runtime querying
Good throughput for materialization. Optimized for batch mapping from tabular/JSON/XML sources
Engine for constructing RDF knowledge graphs from heterogeneous data sources using R2RML and RML mapping languages with YARRRML support
Streaming design handles large files well. No SPARQL engine - pair with Comunica for queries
Fast for JavaScript - streaming architecture keeps memory low. One of the fastest JS RDF parsers
Lightning-fast, spec-compatible, streaming RDF parser/writer for JavaScript following the RDF/JS specification
GPL v3 (Community) / Commercial (Enterprise) + Apache 2.0 (n10s)
Neo4j itself scales very well. The RDF layer (n10s) adds import overhead but queries run at native speed
Fast native graph queries via Cypher. RDF import has overhead from property graph conversion
Not a semantic graph database - Neo4j is a property graph. RDF triples are converted to labeled property graph structures on import, losing native RDF semantics. No SPARQL support, no OWL reasoning, no semantic inferencing. Consider a native triple store if your use case requires semantic knowledge graphs.
Property graph database with the neosemantics (n10s) plugin for RDF import/export, SHACL validation, and ontology mapping
Designed for ontology auditing, not data-scale operations
Web service - analysis typically completes in seconds for normal ontologies
Web-based ontology pitfall scanner that detects common modeling errors and quality issues in OWL/RDF ontologies
Scales with the relational database underneath - no data duplication, no separate RDF storage needed
Query speed depends on the underlying SQL database. Translation overhead is minimal for simple queries
Open-source Virtual Knowledge Graph system that translates SPARQL queries to SQL over relational databases using R2RML mappings
Scales with the relational source - no separate RDF storage
Visual mapping tool - performance depends on the underlying database and Ontop engine
Low-code GUI for designing R2RML-compliant mappings for virtual and materialized RDF graphs from relational databases
Commercial (per-processor or named-user-plus)
Inherits Oracle Database scalability - handles billions of triples in production. RAC clustering for horizontal scaling
Backed by Oracle Database query optimizer; benefits from mature indexing and parallel execution on large datasets
Native RDF graph support built into Oracle Database, storing triples as relational data with full SPARQL 1.1 query/update, RDFS/OWL 2 EL inference, and SHACL validation via an RDF4J adapter. Available on-premise and on Oracle Cloud.
SQLite backend helps with larger ontologies but still limited by Python performance
Slow for RDF I/O but convenient for ontology work. Automatic reasoning via HermiT/Pellet
Python module for ontology-oriented programming with transparent access to OWL ontologies, automatic reasoning via HermiT/Pellet
Disk-backed via RocksDB, handles tens of millions of triples well. Not designed for billion-triple scale
Good read/write performance from Rust + RocksDB. Mid-pack on SPARQL queries - solid but not the fastest
SPARQL graph database written in Rust, available as embedded library, standalone server, and Python/JS bindings
Single-node only. Good for millions of triples, not designed for distributed scale
Same Rust engine as Oxigraph library, with minimal HTTP overhead. Good for small to medium deployments
Standalone SPARQL server built on Oxigraph with HTTP endpoint, Graph Store Protocol support, and built-in YASGUI interface
In-memory reasoning - bounded by ontology complexity more than triple count
Comprehensive reasoner but can be slow on large, complex ontologies. Explanation support is unique
OWL 2 DL reasoner supporting consistency checking, classification, explanation of inferences, and SPARQL-DL queries
Cloud-native enterprise platform. Handles large organizational taxonomies and content volumes
Enterprise platform - optimized for taxonomy workflows, NLP enrichment, and content classification
Enterprise semantic suite for taxonomy, thesaurus, and ontology management with text mining, linked data publishing, and knowledge modeling
Designed for ontology engineering, not large-scale data processing
Desktop app - responsive for typical ontologies. Can slow down on very large ontologies (>100K axioms)
Leading open-source ontology editor from Stanford University with full OWL 2 support, reasoning, and extensive plugin ecosystem
Designed for billions of triples. Handles full Wikidata (~18B triples) on a single machine
Exceptionally fast SPARQL - often the fastest in benchmarks, especially on complex queries at scale
High-performance SPARQL engine from University of Freiburg, capable of handling hundreds of billions of triples on a single machine
Indexes 17B+ triples (full Wikidata) on a single machine with 10GB RAM and 600GB SSD
Optimized for large-scale read-heavy workloads. HDT-based partition delivers fast query execution
Highly scalable triple store using HDT for read-optimized storage with a write partition based on RDF4J. Can index and query the entire Wikidata dump on commodity hardware
Admin tool - scales with the underlying RDF4J server
Web UI - query performance depends on the RDF4J repository backend
Web-based administration and query interface for Eclipse RDF4J Server with repository management, SPARQL queries, and data exploration
In-memory graph - suitable for small to moderate datasets
Moderate .NET performance. Lighter weight than dotNetRDF with simpler API
Lightweight .NET library for Semantic Web applications supporting RDF modeling, SPARQL querying, SHACL validation, and SKOS
In-memory, so bounded by RAM. Designed for high-performance reasoning, not raw storage scale
Extremely fast - among the fastest triple stores for both loading and querying with reasoning
High-performance in-memory knowledge graph by Oxford Semantic Technologies (Samsung) with incremental reasoning and Datalog support
Handles moderate-sized mapping jobs. For very large sources, consider streaming alternatives
Moderate throughput for mapping. Java-based with room for optimization on large sources
Reference implementation of the RML specification for generating RDF from heterogeneous data sources (CSV, JSON, XML, databases)
Streaming parser with constant memory usage. Handles arbitrarily large files
Fast C parser - still one of the quickest for RDF/XML. Battle-tested in production since the 2000s
RDF Parser Toolkit for C providing parsers and serializers for RDF/XML, N-Triples, Turtle, RSS, Atom, and other formats
Multiple storage backends (BDB, MySQL, PostgreSQL) offer different scale characteristics
Native C performance for parsing - historically fast. Showing its age vs. modern Rust alternatives
Modular C libraries providing RDF graph API (librdf), parser/serializer (Raptor), and SPARQL query engine (Rasqal) with bindings for many languages
Commercial (SAP HANA Cloud subscription)
Commercial (consumption-based cloud pricing)
Cloud-native with SAP HANA Cloud scaling. Aimed at enterprise datasets integrated with SAP business data
Runs inside HANA's in-memory columnar engine; designed for fast analytical and graph queries at enterprise scale
RDF triple store and SPARQL engine built into SAP HANA Cloud, with RDFS/OWL reasoning and SHACL validation. Launched in 2025 as part of SAP's push into enterprise knowledge graphs.
Designed for interactive use, not bulk processing
Web-based tool - validates and generates documentation quickly for typical ontology sizes
Free online SHACL toolkit by Sparna for validation, documentation generation, UML diagram generation, and SHACL profile derivation from RDF data
Designed for testing, not production validation
Browser-based - instant validation for small shapes and data graphs
Online interactive tool for testing and validating SHACL shapes against RDF data graphs directly in the browser
In-memory by default. Good for moderate datasets where type safety and API design matter
Rust performance for parsing and graph operations. Less mature than Oxigraph but promising
Rust toolkit for RDF 1.2 and Linked Data providing a generic API with traits for graphs, datasets, parsers, and serializers
Designed for industrial ontology management. Scales with the backing triple store
Web-based tool for ontology management and KG construction workflows
Open-source suite for managing thesauri and ontologies (SKOS, OWL, RDF) with graph visualization, knowledge graph construction from tabular data, and a no-code visual interface
Stardog Free (1-year renewable license) + Commercial Enterprise
Enterprise-proven, handles billions of triples in cluster deployments
Good query performance with reasoning enabled. Virtual graph feature avoids data duplication
Enterprise knowledge graph platform combining graph database with AI toolkit, supporting SPARQL, GraphQL, and OWL reasoning
Disk-based with persistent indexing; designed for large-scale RDF datasets
Very fast SPARQL processing through tensor-based storage and worst-case optimal join algorithms
High-performance tensor-based RDF triple store from the DICE research group (Paderborn University) using worst-case optimal joins for fast SPARQL query processing.
Inherits scale from the backing data platform. No data duplication needed
Queries run against the underlying data platform (Snowflake, Spark, etc.) with ontological reasoning added on top
Virtual Knowledge Graph / SQL Semantic Layer
SQL-based ontology semantic layer that models knowledge graphs over existing databases (Snowflake, Databricks, Spark, etc.) using OWL ontologies, queryable via standard SQL
Document-level processing - not a bulk data tool
Fast Java JSON-LD processing. Focused and optimized for the JSON-LD spec
JSON-LD 1.1 processor and API for Java supporting expansion, compaction, flattening, framing, and RDF conversion
Desktop tool - suited for ontology engineering, not large-scale data processing
Full-featured IDE with good performance for modeling tasks
Enterprise-class IDE for RDFS/OWL modeling, SHACL validation, SPARQL editing, and semantic application development by TopQuadrant
Enterprise-grade with support for large organizational vocabularies and metadata
Enterprise platform - performance tuned for governance workflows, not raw RDF processing
Enterprise data governance platform built on RDF knowledge graphs for metadata, vocabulary, reference data, and business glossary management
Scales with Jena - suitable for production validation workloads
Good Java performance for validation. Efficient for programmatic SHACL checking in pipelines
Open-source Java SHACL implementation based on Apache Jena for constraint checking and rule-based inference
Enterprise-grade. Handles the largest government linked data deployment in the Netherlands
Optimized for linked data serving with SPARQL, REST, and ElasticSearch-based text search
Commercial linked data platform for knowledge graph hosting, SPARQL endpoints, API publishing, and data management. Powers the Dutch Land Registry (Kadaster) linked data infrastructure
Designed for editing, not processing large files
Browser-based editor - responsive for typical Turtle files
Browser-based Turtle syntax editor with syntax highlighting, validation, and prefix autocompletion for writing RDF
Battle-tested at scale - powers DBpedia and many LOD endpoints. Handles billions of triples
Fast ingestion and good query performance. Column store architecture helps analytical queries
High-performance multi-model RDBMS, data integration middleware, and linked data deployment platform by OpenLink Software
Simple web service - validates small RDF/XML documents quickly
Official W3C service for checking and visualizing RDF/XML documents, generating triple listings and graph diagrams
Designed for collaborative ontology development, not large data
Web-based - responsive for normal-sized ontologies. Simpler than desktop Protege
Web-based collaborative ontology editor from Stanford with sharing, discussion threads, change tracking, and revision history
Browser-based visualization - best for ontologies under a few hundred classes
Responsive for small-to-medium ontologies. Force-directed layout slows with many classes (>500)
Web application for interactive visualization of ontologies using the VOWL notation with force-directed graph layout
Browser-based editor - query speed depends entirely on the SPARQL endpoint, not YASGUI itself
Popular web-based SPARQL query editor and result visualizer with syntax highlighting, autocompletion, and multiple output formats
Designed for ontology editing, not data processing
Web-based editor - responsive for typical ontologies
Web-based collaborative ontology editor backed by GitHub for version control, producing Turtle format ontology definitions
Proxy architecture - scales with the backing triple store
Lightweight Node.js server - fast for serving individual resources. Proxies to backend SPARQL store
Lightweight Linked Data server and proxy for dereferencing URIs over HTTP with built-in SPARQL proxy, YASGUI, and graph explorer
In-memory store struggles above ~5M triples. Best as a client library connecting to external stores
Moderate for .NET - pure managed code. I/O is reasonable but SPARQL is slower than Java alternatives
Complete .NET library for parsing, managing, querying, and writing RDF, with SPARQL support and connectors to major triple stores
Client-side tool for exploration and querying. Visualization handles moderate graph sizes
Desktop IDE - query speed depends on the connected database backend
Multi-database graph IDE supporting SPARQL, Cypher, and Gremlin with visual query building, graph exploration, and schema visualization. Connects to Fuseki, Virtuoso, QLever, Neptune, and more
Streaming architecture handles arbitrarily large files. Parser only - no query engine
High-performance streaming parser for JavaScript. Claims fastest JS RDF throughput
High-performance collection of RDF libraries for JavaScript with streaming parsers/writers for Turtle, N-Triples, N-Quads, and TriG
Client-side tool - suitable for individual documents, not large datasets
Browser-based - handles small to medium RDF documents for visualization and validation
Online RDF graph visualizer supporting Turtle, JSON-LD, TriG, and RDF-star with validation, conversion, and generation features
Processes individual JSON-LD documents - not a bulk data tool
Adequate for JSON-LD operations. Not designed for high-throughput RDF processing
JSON-LD 1.1 processor and API implementation for JavaScript (Node.js and browser)
Open Source (core) + Commercial (advanced features)
In-memory by default (bounded by RAM), but disk mode handles billions of triples for larger-than-memory datasets
Very fast - Rust core with Polars/Arrow gives near-native I/O and sub-second SPARQL on millions of triples
Python/Rust Library + Triple Store (disk mode)
Rust-powered Python library for building, querying, and transforming RDF knowledge graphs from Polars DataFrames using OTTR templates. In-memory or persistent disk-backed triple store. Core mapping, SPARQL, and serialization are open source (Apache 2.0). SHACL validation, Datalog reasoning, and disk mode require a separate proprietary license.
Commercial (Community Edition available)
Community Edition (free) + Commercial Enterprise
Scales with the underlying triple store. Proven in large enterprise deployments
Flexible backend — can sit on top of GraphDB, Neptune, Stardog, or its own built-in store. Query speed depends on the chosen backend
Enterprise knowledge graph platform by metaphacts for building knowledge-driven applications on top of existing triple stores. Low-code UI framework with reusable semantic components, SPARQL editor, LDP support, and graph visualization
Inherits rdflib's limitations - validation on large graphs can be slow
Validation speed depends on shape complexity and rdflib backend. Adequate for typical ontologies
Python SHACL validator built on rdflib with support for SHACL rules, advanced path expressions, and SPARQL-based constraints
Stream-based pipelines handle moderate data. Not for high-volume production
Convenience-focused rather than performance-focused. Good DX, moderate throughput
Developer-friendly RDF/JS extension library providing high-level APIs for working with RDF data in JavaScript
Struggles above ~1M triples due to memory and CPU. Not suitable for production-scale workloads
Slow - pure Python, 10-100x slower than native alternatives. Fine for small datasets and prototyping
Pure Python library for working with RDF, including parsers/serializers for various formats, a graph interface, and a SPARQL engine
Works with any RDF-enabled endpoint or embedded structured data. Scales with the connected data source
Client-side processing - performance depends on browser and page complexity
Browser extension and SDK for Linked Data exploration, SPARQL query construction, RDF visualization, and knowledge graph discovery. Provides structured data extraction, entity reconciliation, and semantic data browsing directly in the browser