Semantic reasoner

A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms. The notion of a semantic reasoner generalizes that of an inference engine, by providing a richer set of mechanisms to work with. The inference rules are commonly specified by means of an ontology language, and often a description logic language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining and backward chaining. There are also examples of probabilistic reasoners, including Pei Wang's non-axiomatic reasoning system,[1] and probabilistic logic networks.[2]

List of semantic reasoners

Existing semantic reasoners and related software:

Free software (open source)

  • CEL, OWL 2 EL reasoner (Apache 2)
  • Cwm, a forward-chaining reasoner used for querying, checking, transforming and filtering information. Its core language is RDF, extended to include rules, and it uses RDF/XML or N3 serializations as required. (W3C software license)
  • Drools, a forward-chaining inference-based rules engine which uses an enhanced implementation of the Rete algorithm. (Apache license 2.0)
  • ELK, OWL 2 EL reasoner (Apache 2)
  • EYE, a semi-backward chaining reasoning engine, supporting Euler paths, and via N3 interoperable with Cwm. (MIT license)
  • FaCT++ Reasoner, a tableaux-based reasoner for expressive Description Logics (DL), covering OWL and OWL 2 but lacking support for key constraints and some datatypes. Written in C++. (LGPL)
  • Flora-2, an object-oriented, rule-based knowledge-representation and reasoning system. (Apache 2.0)
  • Gandalf, open-source decision rules engine on PHP (GPL).
  • HermiT, OWL 2 DL reasoner (LGPL)
  • jcel, OWL 2 EL reasoner (LGPL / Apache 2)
  • Jena (framework), an open-source semantic-web framework for Java which includes a number of different semantic-reasoning modules. (Apache License 2.0)
  • OpenRules, an open source business rules and decision management system. Along with a sequential rule engine, includes an inferential rule engine that utilizes a constraint solver
  • Pellet, OWL 2 DL reasoner (AGPL, commercial option available)
  • Prova, a semantic-web rule engine which supports data integration via SPARQL queries and type systems (RDFS, OWL ontologies as type system). (GNU GPL v2, commercial option available)
  • RACER, OWL 2 DL reasoner (BSD-3)
  • RDFSharp, a lightweight C# framework designed to ease the creation of .NET applications based on the RDF model, representing a straightforward didactic solution for start playing with RDF and Semantic Web concepts. With RDFSharp it is possible to realize .NET applications capable of modeling, storing and querying RDF data. (Apache License 2.0)

Free to use (closed source)

  • Cyc inference engine, a forward and backward chaining inference engine with numerous specialized modules for high-order logic. ( ResearchCyc) ( OpenCyc)
  • KAON2 is an infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies.
  • Internet Business Logic (software)—A reasoner designed for end-user app authors. Automatically generates and runs complex networked SQL queries. Explains the results in English at the end-user level.

Commercial software

  • Bossam (software), a RETE-based rule engine with native supports for reasoning over OWL ontologies, SWRL rules, and RuleML rules.
  • RacerPro
  • OntoBroker is an inference engine with native reasoning over F-Logic, ObjectLogic, RIF, and OWL. (, W3C-listed inference engine)

Applications that contain reasoners

  • SemanticMiner includes the OntoBroker reasoner to perform ontology-based semantic search.
  • SemanticGuide is an OntoBroker based expert system.
  • Apache Marmotta includes a rule-based reasoner in its KiWi triple store.
  • dot15926 Editor—Ontology management framework initially designed for engineering ontology standard ISO 15926. Allows Python rule scripting and pattern-based data analysis. Supports extensions.

See also


  1. Wang, Pei. "Grounded on Experience Semantics for intelligence, Tech report 96". CRCC. Retrieved 13 April 2015.
  2. Goertzel, Ben; Iklé, Matthew; Goertzel, Izabela Freire; Heljakka, Ari (2008). Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference. Springer Science & Business Media. p. 42. ISBN 9780387768724.
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