7 Ontologies, RDF, and OWL
The trend is to use the word “ontology” for more complex, and possibly quite be added to the RDF data, describing the fact that the relationship described as. OWL: Web Ontology Language. Page 3. 2. RDF: Resource Description Framework. Key Concepts of RDF. Three views Relations between resources. The relation in this example is between the in the course of evolution of an ontology.
However, for simplicity, we will illustrate most of our use cases assuming a single additional individual. We can handle more individuals in exactly the same way.
One common solution to this problem pattern 1 is to represent the relation as a class rather than a property. Individual instances of such classes correspond to instances of the relation. Additional properties provide binary links to each argument of the relation.
Ontologies - W3C
We can model examples 12and 3 above using this pattern. Similarly, in the example 3 the instance of a class Purchase would represent the fact that John bought the book "Lenny the Lion" from books. The second solution pattern 2 is to represent several individuals participating in the relation as a collection or an ordered list. We use this solution when the order of the arguments of the n-ary relation is important in the model, as in the example 4 above.
Vocabulary for n-ary relations in RDF and OWL The task force plans to produce a suggested vocabulary for describing that a class represents an n-ary relation and for defining mappings between n-ary relations in RDF and OWL and other languages.
A note on this vocabulary is forthcoming.
semantic web - What is the difference between RDF Schema and Ontology? - Stack Overflow
Introducing a new class for a relation We present a pattern where we create a new class and n new properties to represent an n-ary relation. An instance of the relation linking the n individuals is then an instance of this class. We consider three use cases for this pattern, illustrated by examples above. These classes denote natural concepts that are shared or at least understood by users familiar with universities all over the world.
RDF and SPARQL: Using Semantic Web Technology to Integrate the World's Data
A class has a set of instances the individuals in this class. Dupond is an instance of the class: The ontology also includes relationships between classes, that denote natural relationships between individuals in the real world. For instance, the university ontology includes the relationship, e.
Relationships also have instances, e.
CSis an instance of: TeachesIn that has the meaning that Dupond teaches CS Class or relationship instances form the database. Let us now turn to the knowledge base.
Perhaps the most fundamental constraint considered in this context is the subclass relationship. For instance, by stating that the class: Professor is a subclass of the class: AcademicStaff, one expresses a knowledge that is shared with the university setting: Stating a subclass relationship between the class: AcademicStaff and the class: Staff expresses that all the members of the academic staff, in particular the professors, belong to the staff of the university.
So, in particular, from the fact that: Professor, we also know that he is an instance of: It is usual to represent the set of subclass statements in a graphical way by a class hierarchy also called a taxonomy. A class hierarchy Besides the class hierarchy, a very important class of ontology constraints allows fixing the domains of relationships. AcademicStaff and Y to: Department indicate the nature of participants in different relationships. A wide variety of other useful constraints are supported by ontology languages.
Disjointness constraints between classes such as the classes: Staff are disjoint, i. Key constraints for binary relations such as each department must have a unique manager Domain constraints such as only professors or lecturers may teach undergraduate courses. We will show how to give a precise formal semantics to these different kinds of constraints based on logic. The use of logic enables reasoning.
For instance, from the fact that Dupond leads the CS department and the university ontology, it can be logically inferred that: Indeed, such a reasoning based on ontologies and inference rules is one of the main topics of this chapter.
But before delving in technicalities on inference, we devote the remaining of the section to illustrations of the usefulness of inference. Inference is first very useful for query answering. Dupond lives in Paris. He should be in the answer. Because he lives in Paris and because he is a professor. Note however that the only explicit facts we may have for: Dupond are that he lives in Paris and that he is a professor. Some applications need an agreement on common terminologies, without any rigor imposed by a logic system.
Finally, some applications may need more complex ontologies with complex reasoning procedures. It all depends on the requirements and the goals of the applications. To satisfy these different needs, W3C offers a large palette of techniques to describe and define different forms of vocabularies in a standard format.Neo4j Online Meetup #2: Graph Databases, RDF, and linked data
The choice among these different technologies depend on the complexity and rigor required by a specific application. Examples A general example may help.
- Defining N-ary Relations on the Semantic Web
A bookseller may want to integrate data coming from different publishers. This extra piece of information is, in fact, a vocabulary or an ontologyalbeit an extremely simple one.