engineering

Semantic reasoner

Semantic reasoner is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Semantic reasoner rather than just read about it. In short: 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.

Key takeaways

Semantic reasoner belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Semantic reasoner to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Semantic reasoner from memory before moving on to harder problems.

Reference excerpt

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, and probabilistic logic networks.

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). EYE, a reasoning engine performing forward- and backward-chaining along Euler paths, supporting the Semantic Web Stack and implementing Notation3. 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 a Web Ontology Language reasoner and Protégé desktop plugin for representing and reasoning with defeasible subsumption. 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.

Worked examples

Example 1 — a first encounter with Semantic reasoner

Start with the simplest possible case. Write down what Semantic reasoner claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Semantic reasoner before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Semantic reasoner ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Semantic reasoner

In research: Semantic reasoner appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Semantic reasoner in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Semantic reasoner is common in secondary-school and first-year university syllabi. It links to neighbouring topics Automated reasoning, Knowledge engineering, Knowledge representation, so understanding it makes those chapters shorter.
In everyday life: Look for Semantic reasoner outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Semantic reasoner in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Semantic reasoner means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Semantic reasoner out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Semantic reasoner in simple terms?

Why does Semantic reasoner matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Semantic reasoner?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Semantic reasoner.