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Guest Editors' Introduction: Ontologies

William , USC Information Sciences Institute
Austin , Artificial Intelligence Applications Institute, University of Edinburgh

Pages: pp. 18-19

The American Heritage Dictionary defines "ontology" as "the branch of metaphysics that deals with the nature of being." The term has recently been adopted by the artificial intelligence community to refer to a set of concepts or terms that can be used to describe some area of knowledge or build a representation of it. An ontology can be very high-level, consisting of concepts that organize the upper parts of a knowledge base, or it can be domain-specific, such as an ontology of vehicles.

The distinction between an ontology and a knowledge base should be clarified. To us, an ontology provides the basic structure or armature around which a knowledge base can be built. An ontology provides a set of concepts and terms for describing some domain, while a knowledge base uses those terms to represent what is true about some real or hypothetical world. Thus, a medical ontology might contain definitions for terms such as "leukemia" or "terminal illness," but it would not contain assertions that a particular patient had some disease, although a knowledge base might.

Interest in ontologies has grown as researchers and system developers have become more interested in reusing or sharing knowledge across systems. Currently, one key impediment to sharing knowledge is that different systems use different concepts and terms for describing domains. These differences make it difficult to take knowledge out of one system and use it in another. If we could develop ontologies that could be used as the basis for multiple systems, they would share a common terminology that would facilitate sharing and reuse. Developing such reusable ontologies has been an important goal of ontology research. Similarly, if we could develop tools that would support merging ontologies and translating between them, sharing would be possible even between systems based on different ontologies.

The interest in ontologies and their use was evident in the huge response we had to the call for papers for this special issue. We found ourselves in the fortunate position of having many high-quality papers that we wanted to accept for what is obviously an important and active area of work. However, we also found that we have more material than can fit in one issue of Intelligent Systems. So, this issue and part of the next will be devoted to ontologies.

This issue opens with an overview of ontology research by B. Chandrasekaran, John R. Josephson, and V. Richard Benjamins. Following this survey, three articles describe tools and techniques for building ontologies and experiences in using them. Gleb Frank, Adam Farquhar, and Richard Fikes describe their approach to constructing a large knowledge base (with an underlying formal ontology) by extracting it from an existing online resource, the CIA World Fact Book. Andre Valente, Thomas Russ, Robert MacGregor, and William Swartout present a case study of their experience in building and using an ontology for air campaign planning, and then reusing that ontology in a new application. Finally, Mariano Fernández López, Asunción Gómez Pérez, Alejandro Pazos Sierra, and Juan Pazos Sierra describe the Methontology framework and Ontology Design Environment for ontology construction, and show how they used them to construct an ontology in the chemicals domain.


Ontologies will fundamentally change the way in which systems are constructed. Today, knowledge bases are built with little sharing or reuse—each one starts from a blank slate. In the future, intelligent systems developers will have libraries of ontologies at their disposal. Rather than building from scratch, they will assemble knowledge bases from components drawn from the libraries. This should greatly decrease development time while improving the robustness and reliability of the resulting knowledge bases. We hope these two issues of Intelligent Systems will provide a glimpse of that future—and of some of the problems and issues that must be addressed before it can be attained.

About the Authors

William Swartout is the director of the Intelligent Systems Division and an associate research professor of computer science at USC's Information Sciences Institute. He has been involved in the research and development of AI systems for over 20 years. His particular research interests include explanation and text generation, knowledge acquisition, knowledge representation, knowledge sharing, and the development of new AI architectures. He received his PhD and MS in computer science from the Massachusetts Institute of Technology and his bachelor's degree from Stanford University. He is a Fellow of the AAAI, has been elected to the AAAI's Board of Councilors, and is past Chair of SIGART, the ACM's Special Interest Group on Artificial Intelligence. Contact him at USC/ISI, 4675 Admiralty Way, Ste. 1001, Marina del Rey, CA 90292-6695;;
Austin Tate is the technical director of the Artificial Intelligence Applications Institute and holds the Personal Chair in Knowledge-Based Systems at the University of Edinburgh. He helped form the AIAI in 1984 and was its overall director until 1991. As well as engaging in the research, development, and application of knowledge-based methods, he also has a background in databases and software engineering. He graduated in computer studies from the University of Lancaster and received his PhD in machine intelligence at the University of Edinburgh. He is a Chartered Engineer and an elected Fellow of the AAAI. Contact him at the AIAI, Univ. of Edinburgh, 80 South Bridge, Edinburgh EH1 1HN, UK;;
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