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, we discuss these questions and describe some emerging technologies that provide answers. In the final section, we mention some additional issues and summarize the key points of the paper. (For more information on agent-based software engineering, see [Genesereth 1989] and [Genesereth 1992]. See also [Shoham 1993] for a description of a variation of agent-based software engineering known as "agent-oriented programming".) 2. Agent Communication Language

Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation, and knowledgebase development. We have developed a set of tools and services to support the process of achieving consensus on common shared ontologies by geographically distributed groups. These tools make use of the worldwide 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 Ontolingua Server may be accessed through the URL http://ontolingua.stanford.edu/

ion and delegation: Agents can be made extensible and composable in ways that common iconic interface objects cannot. Because we can "communicate" with them, they can share our goals, rather than simply process our commands. They can show us how to do things and tell us what went wrong (Miller and Neches 1987). . Flexibility and opportunism: Because they can be instructed at the level of 16 BRADSHAW goals and strategies, agents can find ways to "work around" unforeseen problems and exploit new opportunities as they help solve problems. . Task orientation: Agents can be designed to take the context of the person's tasks and situation into account as they present information and take action. . Adaptivity: Agents can use learning algorithms to continually improve their behavior by noticing recurrent patterns of actions and events. Toward Agent-Enabled System Architectures In the future, assistant agents at the user interface and resource-managing agents behind the scenes will increas...

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widespread, standardisation of ontologies, message content and message protocols will be necessary. In this paper, we present a lifecycle of a business-to-business e-commerce interaction, and show how the Semantic Web can support a service description language that can be used throughout this lifecycle. By using DAML+OIL, we develop a service description language su#ciently expressive and flexible to be used not only in advertisements, but also in matchmaking queries, negotiation proposals and agreements. We also identify which operations must be carried out on this description language if the B2B lifecycle is to be fully supported. We do not propose specific standard protocols, but instead argue that our operators are able to support a wide variety of interaction protocols, and so will be fundamental irrespective of which protocols are finally adopted.

The word integration has been used with different meanings in the ontology field. This article aims at clarifying the meaning of the word "integration" and presenting some of the relevant work done in integration. We identify three meanings of ontology "integration": when building a new ontology reusing (by assembling, extending, specializing or adapting) other ontologies already available; when building an ontology by merging several ontologies into a single one that unifies all of them; when building an application using one or more ontologies. We discuss the different meanings of "integration", identify the main characteristics of the three different processes and propose three words to distinguish among those meanings: integration, merge and use.

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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.