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Sample Knowledge Bases

Take a look at our on-line examples of knowledge bases:

Canadian mammals
This knowledge base contains facts about all the kinds of wild mammals in Canada. It was created from information contained in the book The Mammals of Canada by A.W.F. Banfield, published by the University of Toronto Press in 1981 and from the Hinterland Who's Who web site of the Canadian Wildlife Service.

Object-oriented software engineering
This knowledge base was created from the book "Object Oriented Software Engineering: Practical Software Development Using UML and Java" by Timothy C. Lethbridge and Robert Laganière to be published by McGraw Hill in Summer 2001. The book is designed as a textbook for second-year university students specializing in software engineering. It should be useful, however, for anyone who has a background in programming and wants to learn practical software engineering techniques.

This is an example of a very large knowledge base, it contains 3122 subjects and 17107 facts on astronomy and physics. Most of the definitions are derived from the Level 5 Knowledge Base. The concept hierarchy and 'part of' hierarchy were authored by John Talbot as a proposal for an astronomy Ontology for the semantic web. The International Virtual Observatory are undertaking a similar effort. We also published a smaller meteorites knowledge base.

This knowledge base is an example of what we refer to as a 'docubase'. A knowledge base about the Java programming language is linked to an associated natural language web document. If you are in the knowledge base and see a link icon beside a fact, clicking on it will take you to that fact in the web document. Conversely, if you are in the web document and see a link, it will take you to that subject in the knowledge base. This combination of a web document and a knowledge base is referred to as a docubase.

This knowledge base is derived from the IEEE Suggested Upper Merged Ontology. SUMO is a kind of standard upper ontology that will promote data interoperability, information search and retrieval, automated inferencing, and natural language processing. This Ontology is limited to concepts that are meta, generic, abstract or philosophical, and hence are general enough to address (at a high level) a broad range of domain areas. Concepts specific to particular domains are not included, but the Ontology does provide a structure upon which ontologies for specific domains (e.g. medicine, finance, engineering, etc.) can be constructed.

© Fact Guru, 2003