ontology

In International Journal Human-Computer Studies 43, p.907-928. Substantial revision of paper presented at the International Workshop on Formal Ontology, March, 1993, Padova, Italy. Available as Technical Report KSL 93-04, Knowledge Systems Laboratory, Stanford University. Thomas R. Gruber Stanford Knowledge Systems Laboratory 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria. 1 Ontologies are often equated with taxonomic hierarchies of classes, but class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972) . To specify a conceptualization one needs to state axioms that do constrain the possible interpretations for the defined terms. Tradeoffs Ontology design, like most design problems, will require making tradeoffs among the criteria. However, the criteria are not inherently at odds. For example, in the interest of clarity, definitions should restrict the possible interpretations of terms. Minimizing ontological commitment, however, means specifying a weak theory, admitting many 2 An ontology serves a different purpose than a knowledge base, and therefore a different notion of representational adequacy (McCarthy & Hayes, 1969) applies. A shared ontology need only describe a vocabulary for talking about a domain, whereas a knowledge base may include the knowledge needed to solve a problem or answer arbitrary queries about a domain. Second, the concept of units of measure is defined as a set of possible values for the quantity.unit function. This is also a specification of the encoding rather than the world, although it is not as obvious as the double-float vs. real-number example. What we mean by physical quantity does not depend on a commitment to a particular set of units. Therefore, the set of possible units should not be part of the definition of physical- quantity. Third, fixing the set of possible units is a limit on extendibility. The world has many standards for units of measure, and the purposes of this ontology (sharing engineering models and theories across people, domains, and tools) do not sanction a preference for one standard. Therefore, the ontology should allow for the definition of alternate sets of units, and a way to relate them to existing units. Version 2 Consider how we could modify the initial proposal to remove these inadequacies. First, we can reformulate the definition of physical-quantity so that the concepts of magnitude and unit of measure are made explicit as independent classes, magnitude and unit-of- measure . (defrelation PHYSICAL-QUANTITY (<=> (PHYSICAL-QUANTITY ?q) (and (defined (quantity.magnitude ?q)) (magnitude (quantity.magnitude ?q)) (defined (quantity.unit ?q)) (unit-of-measure (quantity.unit ?q)))))) Now we can define the class magnitude to include the class of all real numbers, rather than the class of numbers encodable in floating point format. The class real- number comes from KIF’s