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)
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.
- 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
- Apache Marmotta includes a rule-based reasoner in its KiWi triple store.
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.
See also
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 9780387768724.
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. 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