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.

In numerous distributed environments, including today's World Wide Web, enterprise data management systems, large science projects, and the emerging semantic web, applications will inevitably use the information described by multiple ontologies and schemas. We organized the Workshop on Semantic Integration at the Second International Semantic Web Conference to bring together different communities working on the issues of enabling integration among different resources. The workshop generated a lot of interest and attracted more than 70 participants. Interoperability among applications depends critically on the ability to map between them. Semantic integration issues have now become a key bottleneck in the deployment of a wide variety of information management applications.

In this article, we develop a framework for comparing ontologies and place a number of the more prominent ontologies into it. We have selected 10 specific projects for this study, including general ontologies, domain-specific ones, and one knowledge representation system. The comparison framework includes general characteristics, such as the purpose of an ontology, its coverage (general or domain specific), its size, and the formalism used. It also includes the design process used in creating an ontology and the methods used to evaluate it. Characteristics that describe the content of an ontology include taxonomic organization, types of concept covered, top-level divisions, internal structure of concepts, representation of part-whole relations, and the presence and nature of additional axioms.

Example from the LOOM WORDNet Knowledge Base. At the beginning, we assumed that the hyponymy relation could simply be mapped onto the subsumption relation and that the synset notion could be mapped into the notion of concept. Both subsumption and concept have the usual description logic semantics (Woods and Schmolze 1992). LOOM WORDNET knowledge base are reported in table 1. Fig-ORDNET's noun top Under Territorial_-Dominion, we find Macao and Palestine together with Trust_Territory. The Trust_Territory synset, defined as "a dependent country, administered by a country under the supervision of United Nations," denotes a general kind of country rather than a specific country such as Macao or Palestine.

These tools can be used for a great range of activities in the ontology development process, such as ontology design, browsing and implementation, ontology importation, ontology merge and alignment, and ontology-based resource annotation. The middle tier is based on an application server, which makes it easy to create and add new services. A wide range of services for importing and exporting ontologies are available, as we describe in the next section, and some other services rely on them. WAB) to edit formal axioms and rules. Bibliographic references, synonyms, and acronyms can be attached to any of the aforementioned ontology components.

The emerging Semantic Web focuses on bringing knowledge representationlike capabilities to Web applications in a Web-friendly way. The ability to put knowledge on the Web, share it, and reuse it through standard Web mechanisms provides new and interesting challenges to artificial intelligence. In this paper, I explore the similarities and differences between the Semantic Web and traditional AI knowledge representation systems, and see if I can validate the analogy "The Semantic Web is to KR as the Web is to hypertext." The first comes from a tutorial on expert systems written by Robert Engelmore with Edward Feigenbaum in 1993. Because of the importance of knowledge in expert systems and because the current knowledge acquisition method is slow and tedious, much of the future of expert systems depends on breaking the knowledge acquisition bottleneck and in codifying and representing a large knowledge infrastructure.

In the past, many knowledge representation systems failed because they were too monolithic and didn't scale well, whereas other systems failed to have an impact because they were small and isolated. Along with this tradeoff in size, there is also a constant tension between the cost involved in building a larger community that can interoperate through common terms and the cost of the lack of interoperability. The semantic web offers a good compromise between these approaches as it achieves wide-scale communication and interoperability using finite effort and cost. The semantic web is a set of standards for knowledge representation and exchange that is aimed at providing interoperability across applications and organizations. We believe that the gathering success of this technology is not derived from the particular choice of syntax or of logic.

Where Are the Semantics in the Semantic Web? The most widely accepted defining feature of the semantic web is machine-usable content. By this definition, the semantic web is already manifest in shopping agents that automatically access and use web content to find the lowest air fares or book prices. However, where are the semantics? Most people regard the semantic web as a vision, not a reality--so shopping agents should not "count."