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.

Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.

We address the problem of providing integrated access to diverse and dynamic information sources. We explain how this problem differs from the traditional database integration problem and we focus on one aspect of the information integration problem, namely information exchange. We define an object-based information exchange model and a corresponding query language that we believe are well suited for integration of diverse information sources. We describe how, the model and language have been used to integrate heterogeneous bibliographic information sources. We also describe two general-purpose libraries we have implemented for object exchange between clients and servers.

The goal of the Tsimmis Project is to develop tools that facilitate the rapid integration of heterogeneous information sources that may include both structured and unstructured data. This paper gives an overview of the project, describing components that extract properties from unstructured objects, that translate information into a common object model, that combine information from several sources, that allow browsing of information, and that manage constraints across heterogeneous sites. Tsimmis is a joint project between Stanford and the IBM Almaden Research Center.

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

Abstract. A number of ideas concerning information-integration tools can be thought of as constructing answers to queries using views that represent the capabilities of information sources. We review the formal basis of these techniques, which are closely related to containment algo-rithms for conjunctive queries and/or Datalog programs. Then we com-pare the approaches taken by AT&T Labs ' "Information Manifold " and the Stanford "Tsimmis " project in these terms. 1 Theoretical Background Before addressing information-integration issues, let us review some of the basic ideas concerning conjunctive queries, Datalog programs, and their containment. To begin, we use the logical rule notation from [Ull88]. Example 1. The following: p(X,Z):- a(X,Y) & a(Y,Z). is a rule that talks about a, an EDB predicate ("Extensional DataBase, " or stored relation), and p, an IDB predicate ("Intensional DataBase, " or predicate whose relation is constructed by rules). In this and several other examples, it is useful to think of a as an "arc " predicate defining a graph, while other predicates define certain structures that might exist in the graph. That is, a(X, Y) means there is an arc from node X to node Y. In this case, the rule says "p(X, Z) is true if there is an arc from node X to node Y and also an arc from Y to Z." That is, p represents paths of length 2. In general, there is one atom, the head, on the left of the "if " sign,:- and zero of more atoms, called subgoals, on the right side (the body). The head always has an IDB predicate; the subgoals can have IDB or EDB predicates. Thus, here p(X, Z) is the head, while a(X, Y) and a(Y, Z) are subgoals. We assume that each variable appearing in the head also appears somewhere in the body. This "safety " requirement assures that when we use a rule, we are not left with undefined variables in the head when we try to infer a fact about the head's predicate. We also assume that atoms consist of a predicate and zero or more arguments. An argument can be either a variable or a constant. However, we exclude function symbols from arguments.

The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.

TSIMMIS -- The Stanford-IBM Manager of Multiple Information Sources -- is a system for integrating information. It o ers a data model and a common query language that are designed to support the combining of information from many different sources. It also o ers tools for generating automatically the components that are needed to build systems for integrating information. In this paper we shall discuss the principal architectural features and their rationale.

Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion of the sharing process. We have developed and implemented PROMPT, an algorithm that provides a semi-automatic approach to ontology merging and alignment. PROMPT performs some tasks automatically and guides the user in performing other tasks for which his intervention is required.

Agent software is a rapidly developing area of research. However, the overuse of the word ‘agent ’ has tended to mask the fact that, in reality, there is a truly heterogeneous body of research being carried out under this banner. This overview paper presents a typology of agents. Next, it places agents in context, defines them and then goes on, inter alia, to overview critically the rationales, hypotheses, goals, challenges and state-of-the-art demonstrators of the various agent types in our typology. Hence, it attempts to make explicit much of what is usually implicit in the agents literature. It also proceeds to overview some other general issues which pertain to all the types of agents in the typology. This paper largely reviews software agents, and it also contains some strong opinions that are not necessarily widely accepted by the agent community. 1 1