In Summary:
"An ontology defines a common vocabulary for researchers who need to share information in a domain ... An ontology together with a set of individual instances of classes constitutes a knowledge base. In reality, there is a fine line between where the ontology ends and the knowledge base begins."
From: "A Guide to Creating Your First Ontology" by Natalya F. Noy and Deborah L. McGuiness (Stanford University, Stanford, California) <View PDF file on WWW>
View a definition of ontology.
In Detail:
"Experience shows that the performance of tasks that seem to involve intelligence also seem to require a huge storage of knowledge. This raises the question "What is knowledge?". Informally, knowledge is information about some domain or subject area, or about how to do something.
Humans require and use a lot of knowledge to carry out even the most simple common sense tasks. Computers are very good at tasks which do not require much knowledge, such as simple arithmetic, or sorting. They aren't, as yet, very good at many knowledge-intensive tasks at which humans excel, such as recognising faces in a picture, medical diagnosis, understanding natural language, or legal argumentation. A central issue to these tasks is how to acquire and represent knowledge about some domain, and now to use that knowledge to answer questions and solve problems.
In order to use knowledge and reason with it, one needs what is called a representation and reasoning system (RRS). A RRS is composed of a language to communicate with a computer, a way to assign meaning to the language, and procedures to compute answers given input in the language. Intuitively, an RRS lets one tell the computer something in a language where one has some meaning associated with the sentences in the language, one can ask the computer questions, and the computer will produce answers that can be interpreted according to the meaning associated with the language.
At one extreme, the language could be a low level computer language such as Fortran or C++. In these languages, the meaning of the sentences, the programs, is purely in terms of the steps the computer will carry out to execute the program. What computation will be carried out given a program and some input, is straightforward to determine. How to map from an informal statement of a problem to a representation of the problem in these RRSs, programming, is a difficult task.
At the other extreme, the language could be a natural language, such as English, where the sentences can refer to the problem domain. In this case, the mapping from a problem to a representation is not very difficult: One simply needs to describe the problem in English! However, what computation needs to be carried out in the computer in response to the input is much more difficult to determine.
Knowledge bases operate where the distance from a natural specification of the problem to the representation of the problem is not very far. Hence they fall between the two extremes described above.
What makes a knowledge base into an RRS is the notion of semantics. Semantics allow us to debate the truth of information in a system, and make such information knowledge rather than just data. Although knowledge bases are obviously part of a continuum which includes conventional databases, their contents are more flexible and their procedures for answering queries are more sophisticated than in a database. A database typically has table lookup; one can ask about what is in the database, not about what else must be true, or is likely to be true, about the domain."
From Section 1.3 of "Computational Intelligence: A Logical Approach" by Poole, Mackworth and Goebel (Oxford University Press (1998)). The section is titled "Representation and Reasoning".
<Another abridgement from the same source follows in the next section on ontologies>
In LegendBurster:
In summary, then: LegendBurster's characterisation of a knowledge base is a collection of inter-related data and information, usually with a layered structure, whose inter-relationships are stored in such a way as to make the collection capable of responding to queries expressed at a high level in the data structure, in a manner similar to that expected from an intelligent person given access to the same data and information. This response includes an ability to provide explanations, if requested.
Go to next section: Ontologies