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

Notable applications

Notable semantic reasoners and related software:

Free to use (closed source)

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)

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.
Evrete, a forward-chaining Java rule engine that uses the Rete algorithm and is compliant with the Java Rule Engine API (JSR 94).
D3web, a platform for knowledge-based systems (expert systems).
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.
OWLSharp, a lightweight and friendly .NET library for realizing intelligent Semantic Web applications.
NRules a forward-chaining inference-based rules engine implemented in C# which uses an enhanced implementation of the Rete algorithm
Prova, a semantic-web rule engine which supports data integration via SPARQL queries and type systems (RDFS, OWL ontologies as type system).
DIP, Defeasible-Inference Platform (DIP) is an Web Ontology Language reasoner and Protégé desktop plugin for representing and reasoning with defeasible subsumption.^[3] It implements a Preferential entailment style of reasoning that reduces to "classical entailment" i.e., without the need to modify the underlying decision procedure.

Semantic Reasoner for Internet of Things (open-source)

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.

References

^ Wang, Pei. "Grounded on Experience Semantics for intelligence, Tech report 96". www.cogsci.indiana.edu. CRCC. Retrieved 13 April 2015.
^ 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 978-0-387-76872-4.
^ Britz, K. and Varzinczak, I., (2018). Rationality and context in defeasible subsumption. In International Symposium on Foundations of Information and Knowledge Systems (pp. 114-132). Springer, Cham.

External links

OWL 2 Reasoners listed on W3C SW Working Group homepage
SPARQL Query Language for RDF
Marko Luther, Thorsten Liebig, Sebastian Böhm, Olaf Noppens: Who the Heck Is the Father of Bob?. ESWC 2009: 66-80
Jurgen Bock, Peter Haase, Qiu Ji, Raphael Volz. Benchmarking OWL Reasoners^{[permanent dead link‍]}. Mirror available. In ARea2008 – Workshop on Advancing Reasoning on the Web: Scalability and Commonsense (June 2008)
Tom Gardiner, Ian Horrocks, Dmitry Tsarkov. Automated Benchmarking of Description Logic Reasoners. Description Logics Workshop 2006

[Wang-1] Wang, Pei. "Grounded on Experience Semantics for intelligence, Tech report 96". www.cogsci.indiana.edu. CRCC. Retrieved 13 April 2015.

[Goertzel2008-2] 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 978-0-387-76872-4.

[3] Britz, K. and Varzinczak, I., (2018). Rationality and context in defeasible subsumption. In International Symposium on Foundations of Information and Knowledge Systems (pp. 114-132). Springer, Cham.

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