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.

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Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. We propose the new method FCA--MERGE for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies, that are to be merged. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA--MERGE.

As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important di#erences between database schemas and ontologies. The di#erences stem from di#erent usage paradigms, the presence of explicit semantics, and di#erent knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These di#erences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is di#erent. We must develop automatic techniques for finding similarities and di#erences between versions.

The World Wide Web is emerging not only as an infrastructure for data, but also for a broader variety of resources that are increasingly being made available as Web services. Relevant current standards like UDDI, WSDL, and SOAP are in their fledgling years and form the basis of making Web services a workable and broadly adopted technology. However, realizing the fuller scope of the promise of Web services and associated service oriented architecture will requite further technological advances in the areas of service interoperation, service discovery, service composition, and process orchestration. Semantics, especially as supported by the use of ontologies, and related Semantic Web technologies, are likely to provide better qualitative and scalable solutions to these requirements. Just as semantic annotation of data in the Semantic Web is the first critical step to better search, integration and analytics over heterogeneous data, semantic annotation of Web services is an equally critical first step to achieving the above promise. Our approach is to work with existing Web services technologies and combine them with ideas from the Semantic Web to create a better framework for Web service discovery and composition. In this paper we present MWSAF (METEOR-S Web Service Annotation Framework), a framework for semi-automatically marking up Web service descriptions with ontologies. We have developed algorithms to match and annotate WSDL files with relevant ontologies. We use domain ontologies to categorize Web services into domains. An empirical study of our approach is presented to help evaluate its performance.

With the grown availability of large and specialized online ontologies, the questions about the combined use of independently developed ontologies have become even more important. Although there is already a lot of research done in this area, there are still many open questions. In this paper we try to classify the problems that may arise into a common framework. We then use that framework to examine several projects that aim at some ontology combination task, thus sketching the state of the art. We conclude with an overview of the different approaches and some recommandations for future research. 1

Semantic integration has been a long-standing challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration, and discuss the difficulties underlying the integration process. We then describe recent progress and identify open research issues. We will focus in particular on schema matching, a topic that has received much attention in the database community, but will also discuss data matching (e.g., tuple deduplication), and open issues beyond the match discovery context (e.g., reasoning with matches, match verification and repair, and reconciling inconsistent data values). For previous surveys of database research on semantic integration, see (Rahm & Bernstein 2001;

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies, and describe experiments that show the promise of the approach.

Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. 1 A Motivating Example: the Semantic Web The current World-Wide Web has well over 1.5 billion pages [2], but the vast majority of them are in human-readable format only (e.g., HTML). As Work done while the author was at the University of Washington, Seattle 2 AnHai Doan et al. a consequence software agents (softbots) cannot understand and process this information, and much of the potential of the Web has so far remained untapped. In response, researchers have created the vision of the Semantic Web [5], where data has structure and ontologies describe the semantics of the data. When data is marked up using ontologies, softbots can better understand the semantics and therefore more intelligently locate and integrate data for a wide variety of tasks. The following example illustrates the vision of the Semantic Web. Example 1. Suppose you want to fi

As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files---an approach that does not work for comparing ontologies. Two ontologies can be identical but have different text representation. We have