Knowledge Representation


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Principles

Representing knowledge using logic.

Fundamental trade-off between representation power and computational properties. Fragments of first order logic suited for Knowledge Representation.

We’re listening — tell us what you think

Reasoning algorithms and implementations, and how reasoning is used to support knowledge representation. Ontology languages for the Semantic Web. Non-monotonic logics.


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Such diagrams and disks comprise Lull's Ars Magna Great Art , which was the first attempt to develop mechanical aids to reasoning. It served as an inspiration to the pioneer in symbolic logic, Gottfried Wilhelm Leibniz. For more information about the book, see the top menu at left, which has links to the preface , the table of contents and the index. The web page for the index is keyed to the page numbers of the printed edition, but it also includes hyperlinks to web sites for various people and topics that are mentioned in the book.

The other menus have links to web sites designed for students, teachers, and general readers who are looking for background information related to the book: Logic: a tutorial about sets, functions, and logic; a web site on conceptual graphs ; a link to the ANSI standard for the Knowledge Interchange Format KIF ; and a series of examples represented in each of these notations.

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Extensible Knowledge Representation: the Case of Description Reasoners

A representation of some piece of knowledge is the internal representation of the knowledge. A representation scheme specifies the form of the knowledge. A knowledge base is the representation of all of the knowledge that is stored by an agent.

A good representation scheme is a compromise among many competing objectives. A representation should be.

Many different representation schemes have been designed. Many of these start with some of these objectives and are then expanded to include the other objectives.

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