Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.

If you are interested in writing a review, contact chandra@cis. It is intended to be a "general textbook of knowledge-base analysis and design" (p. Its great strength is recognizing the need for an interdisciplinary approach, and the attempt at presenting the logical and philosophical foundations of knowledge representation under a unified view. Its great weakness is a lack of consistent rigor, which is needed in a textbook for newcomers to a subject. After some historical remarks and a first introductory chapter devoted to logic, Sowa immediately attacks the hard problems involved in choosing ontological categories, which lie at the heart of any knowledge representation project.

However, interoperability among these manufacturing applications is hindered because the applications use different terminology and representations of the domain. These problems arise most acutely for systems that must manage the heterogeneity inherent in various domains and integrate models of different domains into coherent frameworks (figure 1). For example, such integration occurs in businessprocess reengineering, where enterprise models integrate processes, organizations, goals, and customers. Even when applications use the same terminology, they often associate different semantics with the terms. This clash over the meaning of the terms prevents the seamless exchange of information among the applications.

It is the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality. Ontology is, put simply, about existence. Like so many things, the term was borrowed by computer science and is rapidly becoming a buzzword in industry, tossed about by salesfolk, like all buzzwords, as if it were something everyone knew about. As it turns out, of course, very few people who use the word actually know what it means, and as a result, the actual meaning has changed, and is changing, over time. All computer scientists who claim allegiance to this field are constantly peppered with the same question, "What is an ontology?"

This ease has spurred an increasing interest from professionals, the general public, and consequently politicians to make publicly available the tremendous wealth of information kept in museums, archives, and libraries--the so-called memory organizations. Quite naturally, their development has focused on presentation, such as web sites and interfaces to their local databases. Now with more and more information becoming available, there is an increasing demand for targeted global search, comparative studies, data transfer, and data migration between heterogeneous sources of cultural contents. The reality of semantic interoperability is getting frustrating. In the cultural area alone, dozens of standard and hundreds of proprietary metadata and data structures exist as well as hundreds of terminology systems.

Research on semantic web services promises greater interoperability among software agents and web services by enabling content-based automated service discovery and interaction and by utilizing. Although this is to be based on use of shared ontologies published on the semantic web, services produced and described by different developers may well use different, perhaps partly overlapping, sets of ontologies. Interoperability will depend on ontology mappings and architectures supporting the associated translation processes. The question we ask is, does the traditional approach of introducing mediator agents to translate messages between requestors and services work in such an open environment? This article reviews some of the processing assumptions that were made in the development of the semantic web service modeling ontology OWLS and argues that, as a practical matter, the translation function cannot always be isolated in mediators.

This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop digital engineering archives. This work is part of an ongoing, multiuniversity effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, work flows, and processes. With these techniques formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability. The article presents examples of how the techniques can be used to encode specific engineering information packages and work flows.

In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications. Commercial organizations are seeking to codify web services using such formalizations as the universal description, discovery, and integration (UDDI) specification. There are efforts to standardize intelligent agent technology, such as the Foundation for Intelligent Physical Agents (FIPA). These efforts at standardization must use ontologies if emerging internet applications are to be powered by semantics, the meaning behind advanced applications and their enterprise-level and community-level transactions. In this article, we discuss some issues that arise when ontologies are used to support corporate application domains such as electronic commerce (e-commerce) and some technical problems in deploying ontologies for realworld use. In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture and provide a short summary of ontology development tools. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications. The "vocabularies" for ontologies, as discussed in the introduction to this special issue, are distinct at different levels.

Knowledge portals provide views onto domainspecific information on the World Wide Web, thus helping their users find relevant, domain-specific information. The construction of intelligent access and the contribution of information to knowledge portals, however, remained an ad hoc task, requiring extensive manual editing and maintenance by the knowledge portal providers. To diminish these efforts, we use ontologies as a conceptual backbone for providing, accessing, and structuring information in a comprehensive approach for building and maintaining knowledge portals. Hence, services flourish that put up knowledge portals for a well-structured orientation on the web. Although there are some general-purpose knowledge portals such as Yahoo, the majority of knowledge portals, however, are domain-or market-specific and serve a particular clientele, for example, Look-Look, which offers structured access to trends in youth culture for companies with an interest in this market.

Sharing data across disparate sources requires solving many problems of semantic integration, such as matching ontologies or schemas, detecting duplicate tuples, reconciling inconsistent data values, modeling complex relations between concepts in different sources, and reasoning with semantic mappings. This issue of AI Magazine includes papers that discuss various methods on establishing mappings between ontology elements or data fragments. The collection includes papers that discuss semantic-integration issues in such contexts as data integration and web services. The issue also includes a brief survey of semantic-integration research in the database community. We refer to this set of problems collectively as semantic integration.