To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.

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.

This paper is intended to serve as a comprehensive introduction to the emerging eld concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to e ective communication among people, organisations, and/or software systems. We showhowthe development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, rst discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the speci cation, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging eld,

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Information integration is enabled by having a precisely defined common terminology. We call this combination of terminology and definitions an ontology. We have developed a set of tools and services to support the process of achieving consensus on such a common shared ontologies by geographically distributed groups. These tools make use of the world-wide web to enable wide access and provide users with the ability to publish, browse, create, and edit ontologies stored on an ontology server. Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information.

The European GALEN project is developing terminology services based on a large, re-usable medical ontology. The ontology is being built using GRAIL, a description logic with transitivity and general concept inclusions.

Despite years of work, no re-usable clinical terminology has yet been demonstrated in widespread use. This paper puts forward ten reasons why developing such terminologies is hard. All stem from underestimating the change entailed in using terminology in software for ‘patient centred ’ systems rather than for its traditional functions of statistical and financial reporting. Firstly, the increase in scale and complexity are enormous. Secondly, the resulting scale exceeds what can be managed manually with the rigour required by software, but building appropriate rigorous representations on the necessary scale is, in itself, a hard problem. Thirdly, ‘clinical pragmatics ’ – practical data entry, presentation and retrieval for clinical tasks – must be taken into account, so that the intrinsic differences between the needs of users and the needs of software are addressed. This implies that validation of clinical terminologies must include validation in use as implemented in software. Why-is-terminology-hard-single-r2.doc 14/01/00 15:21 2 1.

ions and assumptions that will enable the agent to retrieve information that is relevant to a query. . Methods for appropriately combining and summarizing retrieved information. Building such brokers as ad hoc, monolithic applications will not scale, and the resulting brokers will not be able to interoperate with the new protocols and services being developed for the Internet. Technical Approach Our approach in this project is to enable the construction and maintenance of domain-specific information brokers by developing . Detailed specifications of a broker system architecture. . A broker shell that implements the broker architecture and contains implementations of all the architecture's domain-independent brokering facilities. . A domain and source modeling tool kit for developing and maintaining a broker's domainspecific expertise. . Two example brokers: a broker in the Electronic Commerce testbed being built in the CommerceNet project, and a broker in the pharmaceutical domain for...

We describe an interlingua-based methodology for translating encoded knowledge and present a formalism for declaratively specifying vocabulary translations within a predicate logic interlingua. In this paper we (1) use the formalism to provide a semantics for translation, (2) show that the formalism enables translation to be done as deduction by a standard theorem prover, (3) describe a proof technique for determining whether a given set of rules for translating from one vocabulary to another is sufficient for performing that translation for any theory, and (4) describe techniques for precompiling translation rules that translate directly between two given vocabularies. Motivation Acquiring and representing knowledge is the key to building powerful intelligent systems. Unfortunately, knowledge base construction is difficult and time consuming. The development of most systems requires a new knowledge base to be constructed from scratch. As a result, most systems remain small to medium ...