What is the difference between RDF and OWL? [closed]

Question

The semantic web comes in layers. This is a quick summary of the ones I think you're interested in.

Update: Please note that RDFS is used to define the structure of the data, not OWL. OWL describes semantic relationships which normal programming, such as a C struct, isn't fussed about and is closer to AI research & set theory.

Triples & URIs

Subject - Predicate - Object

These describe a single fact. Generally URI's are used for the subject and predicate. The object is either another URI or a literal such as a number or string. Literals can have a type (which is also a URI), and they can also have a language. Yes, this means triples can have up to 5 bits of data!

For example a triple might describe the fact that Charles is Harrys father.

<http://example.com/person/harry> <http://familyontology.net/1.0#hasFather> <http://example.com/person/charles> .

Triples are database normalization taken to a logical extreme. They have the advantage that you can load triples from many sources into one database with no reconfiguration.

RDF and RDFS

The next layer is RDF - The Resource Description Framework. RDF defines some extra structure to triples. The most important thing RDF defines is a predicate called "rdf:type". This is used to say that things are of certain types. Everyone uses rdf:type which makes it very useful.

RDFS (RDF Schema) defines some classes which represent the concept of subjects, objects, predicates etc. This means you can start making statements about classes of thing, and types of relationship. At the most simple level you can state things like http://familyontology.net/1.0#hasFather is a relationship between a person and a person. It also allows you to describe in human readable text the meaning of a relationship or a class. This is a schema. It tells you legal uses of various classes and relationships. It is also used to indicate that a class or property is a sub-type of a more general type. For example "HumanParent" is a subclass of "Person". "Loves" is a sub-class of "Knows".

RDF Serialisations

RDF can be exported in a number of file formats. The most common is RDF+XML but this has some weaknesses.

N3 is a non-XML format which is easier to read, and there's some subsets (Turtle and N-Triples) which are stricter.

It's important to know that RDF is a way of working with triples, NOT the file formats.

XSD

XSD is a namespace mostly used to describe property types, like dates, integers and so forth. It's generally seen in RDF data identifying the specific type of a literal. It's also used in XML schemas, which is a slightly different kettle of fish.

OWL

OWL adds semantics to the schema. It allows you to specify far more about the properties and classes. It is also expressed in triples. For example, it can indicate that "If A isMarriedTo B" then this implies "B isMarriedTo A". Or that if " C isAncestorOf D " and " D isAncestorOf E " then " C isAncestorOf E ". Another useful thing owl adds is the ability to say two things are the same, this is very helpful for joining up data expressed in different schemas. You can say that relationship "sired" in one schema is owl:sameAs "fathered" in some other schema. You can also use it to say two things are the same, such as the "Elvis Presley" on wikipedia is the same one on the BBC. This is very exciting as it means you can start joining up data from multiple sites (this is "Linked Data").

You can also use the OWL to infer implicit facts, such as "C isAncestorOf E".

In short:

• RDF defines how to write stuff
• OWL defines what to write

As previous poster wrote, RDF is a specification which tells you how to define triples.

The problem is that RDF allows you to define everything, so you could compose a declaration like this:

| subject | predicate | object |
|---------|-----------|--------|
| Alex    | Eats      | Apples |
| Apples  | Eats      | Apples |
| Apples  | Apples    | Apples |

These triples form valid RDF documents.

But, semantically, you understand that these statements are incorrect and RDF cannot help you to validate what you have written.

This is not a valid ontology.

OWL specification defines exactly what you can write with RDF in order to have valid ontology.

Ontologies can have several properties.

Thats why OWL (ver 1) defines several versions like OWL DL, OWL Lite, OWL Full.

RDF, RDFS and OWL are means to express increasingly complex information or knowledge. All of them can be serialised in RDF/XML syntax (or any other RDF serialisation syntax like Turtle or N3 for instance).

These technologies are related and supposed to be interoperable, yet they have different origins that's maybe why the relation between them is complicated to grasp. The choice on one or the other depends on how much complexity the situation you are modelling requires.

Summary of expressivity

RDF: Straightforward representation, focused on the instances and on the mapping to their types (rdf:type). It is possible to define custom properties to link data and creating triples. RDF data are queried with SPARQL. Example of RDF serialised in Turtle:

@prefix : <http://www.example.org/> .
:john    rdf:type           :Man .
:john    :livesIn  "New-York" .
:livesIn    rdf:type    rdf:Property .

---

RDFS: Some situations are not easily modelled by RDF alone, it is sometimes interesting to represent more complex relations like subclasses (the type of a type) for example. RDFS provides special means to represent such cases, with constructs like rdfs:subClassOf, rdfs:range or rdfs:domain. Ideally, a reasoner can understand the RDFS semantics and expand the number of triples based on the relations: For instance if you have the triples John a Man and Man rdfs:subClassOf Human then you should generate as well the triple John a Human. Note that this is not possible to do with RDF alone. RDFS data are queried using SPARQL. Example of RDFS serialised in Turtle:

@prefix : <http://www.example.org/> .
:john    rdf:type           :Man .
:Man    rdfs:subClassOf    :Human .
:john    :livesIn  "New-York" .
:livesIn    rdf:type    rdf:Property .
# After reasoning
:john    rdf:type    :Human .

---

OWL: The highest level of expressivity. Relation between classes can be formally modelled based on description logics (mathematical theory). OWL relies heavily on the reasoner, it is possible to express complex constructs such as chained properties for instance or restriction between classes. OWL serves to build ontologies or schema on the top of RDF datasets. As OWL can be serialised as RDF/XML, it is theoretically possible to query it via SPARQL, yet it is much more intuitive to query an OWL ontology with a DL query (which is usually a standard OWL class expression). Example of OWL constructs serialised in Turtle.

@prefix : <http://www.example.org/> .
:livesIn    rdf:type    owl:DatatypeProperty .
:Human    rdf:type    owl:Class .
:Man   rdf:type    owl:Class .
:Man    rdfs:subClassOf    :Human .
:John    rdf:type    :Man . 
:John    rdf:type    owl:NamedIndividual .

Firstly, an as has been pointed out before, owl can be serialised in RDF.

Secondly, OWL adds ontological capability to RDF (which on its own only provides extremely limited capability for formal knownledge representation), by providing the apparatus to define the components of your triple using formal computable first order description logic. That is what posters here mean by when they talk about "semantic richness".

Thirdly, it's important to realise that in OWL-Full (for OWL 1) rdfs:class and owl:class are equivalent and in OWL-DL, owl:class is a subclass of rdfs:class. In effect, this means that you can use an OWL ontology as a schema for RDF (which does not formally require schemata).

I hope that helps to clarify further.