Semantic reasoner

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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 non-axiomatic reasoning systems,[1] and probabilistic logic networks.[2]

Notable applications[edit]

Notable semantic reasoners and related software:

Free to use (closed source)[edit]

  • Cyc inference engine, a forward and backward chaining inference engine with numerous specialized modules for high-order logic.
  • KAON2 is an infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies.

Free software (open source)[edit]

  • 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.
  • Drools, a forward-chaining inference-based rules engine which uses an enhanced implementation of the Rete algorithm.
  • Flora-2, an object-oriented, rule-based knowledge-representation and reasoning system.
  • Jena, an open-source semantic-web framework for Java which includes a number of different semantic-reasoning modules.
  • Prova, a semantic-web rule engine which supports data integration via SPARQL queries and type systems (RDFS, OWL ontologies as type system).

Applications that contain reasoners[edit]


Semantic Reasoner for Internet of Things (open-source)[edit]

S-LOR (Sensor-based Linked Open Rules) semantic reasoner S-LOR is under GNU GPLv3 license.  

S-LOR (Sensor-based Linked Open Rules) is a rule-based reasoning engine and an approach for sharing and reusing interoperable rules to deduce meaningful knowledge from sensor measurements.

See also[edit]

References[edit]

  1. ^ Wang, Pei. "Grounded on Experience Semantics for intelligence, Tech report 96". www.cogsci.indiana.edu. 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.

External links[edit]