Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success. Expert

In order to tackle the need of sharing knowledge within and across organisational boundaries, the last decade has seen researchers both in academia and industry advocating for the use of ontologies as a means for providing a shared understanding of common domains. But with the generalised use of large distributed environments such as the World Wide Web came the proliferation of many di#erent ontologies, even for the same or similar domain, hence setting forth a new need of sharing---that of sharing ontologies. In addition, if visions such as the Semantic Web are ever going to become a reality, it will be necessary to provide as much automated support as possible to the task of mapping di#erent ontologies. Although many e#orts in ontology mapping have already been carried out, we have noticed that few of them are based on strong theoretical grounds and on principled methodologies. Furthermore, many of them are based only on syntactical criteria. In this paper we present a theory and method for automated ontology mapping based on channel theory, a mathematical theory of semantic information flow.

The term Semantic Web was coined by Tim Berners-Lee to describe his proposal for "a web of meaning," as opposed to the "web of links" that currently exists on the Internet. Toachieve this vision, we need to develop languages and tools that enable machine understandable web pages. The SHOE project, begun in 1995, was one of the first efforts to explore these issues. In this paper, we describe our experiences developing and using the SHOE language. Webeginby describing the unique features of the World Wide Web and how they must influence potential Semantic Web languages. Then we present SHOE, a language whichallows web pages to be annotated with semantics, describe its syntax and semantics, and discuss our approaches to handling the problems of interoperability in distributed environments and ontology evolution. Finally,weprovide an overview of a suite of tools for the Semantic Web, and discuss the application of the language and tools to two different domains.

We discuss the problems associated with versioning ontologies in distributed environments. This is an important issue because ontologies can be of great use in structuring and querying internet information, but many of the Internet's characteristics, such as distributed ownership, rapid evolution, and heterogeneity, make ontology management difficult. We present SHOE, a web-based knowledge representation language, that supports multiple versions of ontologies. We then discuss the features of SHOE that address ontology versioning, the affects of ontology revision on SHOE web pages, and methods for implementing ontology integration using SHOE's extension and version mechanisms. 1. Introduction As the use of ontologies becomes more prevalent, there is a more pressing need for good ontology management schemes. This is especially true once an ontology has been used to structure data, since changing it can be very expensive. Often the solution is to "get it right the first time", however, i...

We report on an approach to representation and retrieval of information from large textual databases. Our approach is based on dynamic ontologies that are automatically constructed from textual data by a new method combining techniques from knowledge representation, natural language processing, and machine learning. The method learns concepts automatically from documents, and uses them to build domain-specific ontologies and to organize the information contained in the documents. The ontologies generated are dynamic in that they are constantly updated and expanded as new documents are added, requiring minimal supervision from domain experts. Information contained in the documents are efficiently retrieved based on concepts in the ontology, allowing for precision and completeness to be traded off. A prototype implementation has been very encouraging.

One way of designing a taxonomy is by identifying a number of different aspects, or facets of the domain and then designing one taxonomy per facet. In such a faceted taxonomy, the indexing of objects is done by combining terms from di#erent facets. A faceted taxonomy has several advantages by comparison to a single hierarchical taxonomy, such as conceptual clarity, compactness and scalability. However, a major drawback of faceted taxonomies is the possibility of forming a large number of invalid combinations of terms, i.e. combinations of terms that do not apply to any object of the underlying domain. The presence of such invalid combinations causes serious problems during object indexing or browsing. To alleviate this problem, we propose an algebra of taxonomies whose operators allow the e#cient and flexible specification of only valid combinations of terms. This algebra can be used in order to construct very big compound taxonomies in a very systematic and e#cient way.

As ontologies become ever more important for semanticallyrich information exchange and a crucial element for supporting knowledge sharing in a large distributed environment, like the Web, the demand for sharing them increases accordingly. One way of achieving this ambitious goal is to provide mechanised ways for mapping and merging ontologies. This has been the focus of recent research in knowledge engineering.

. Ontobroker applies Artificial Intelligence techniques to improve access to heterogeneous, distributed and semistructured information sources as they are presented in the World Wide Web or organization-wide intranets. It relies on the use of ontologies to annotate web pages, formulate queries and derive answers. In the paper we will briefly sketch Ontobroker. Then we will discuss its main shortcomings, i.e. we will share the lessons we learned from our exercise. We will also show how On2broker overcomes these limitations. Most important is the separation of the query and inference engines and the integration of new web standards like XML and RDF. 1 Introduction The World Wide Web (WWW) currently contains around 300 million static objects providing a broad variety of information sources (cf. [Bharat & Broder, 1998]). The early question of whether a certain piece of information is on the web has become the problem of how to find and extract it. The problem will become even mor...

This paper studies the extraction and transformation problem on documents. Solving this problem entails extracting the structures contained in a document and transforming the structures to make them available for further automatic processing. This paper provides an overview of methods and tools for extracting information and transforming the extracted information as required by the end-user. The overview is based on a taxonomy that classifies both characteristics of the available sources and properties of the extraction techniques. The paper concludes with a perspective on future developments, including a discussion of tools with learning capabilities and the role that XML and other related standards will play in the future.