With the advances in telecommunications, and the introduction of the Internet, information systems achieved physical connectivity, but have yet to establish logical connectivity. Lack of logical connectivity is often inviting disaster as in the case of Mars Orbiter, which was lost because one team used metric units, the other English while exchanging a critical maneuver data. In this Thesis, we focus on the two intertwined sub problems of logical connectivity, namely data extraction and data interpretation in the domain of heterogeneous information systems. The first challenge, data extraction, is about making it possible to easily exchange data among semi-structured and structured information systems. We describe the design and implementation of a general purpose, regular expression based Caméléon wrapper engine with an integrated capabilities-aware planner/optimizer/executioner. The second challenge, data interpretation, deals with the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. We extend the existing formalization of the COIN framework with new logical formalisms and features to handle larger

This paper presents WebODE as a workbench for ontological engineering that not only allows the collaborative edition of ontologies at the knowledge level, but also provides a scalable architecture for the development of other ontology development tools and ontology-based applications. First, we will describe the knowledge model of WebODE, which has been mainly extracted and improved from the reference model of METHONTOLOGY’s intermediate representations. Later, we will present its architecture, together with the main functionalities of the WebODE ontology editor, such as its import/export service, translation services, ontology browser, inference engine and axiom generator, and some services that have been integrated in the workbench: WebPicker, OntoMerge and the OntoCatalogue.

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.

A system that enables interoperation among information sources using ontologies needs to resolve the terminological differences between ontologies. In this work, we present several methods that we have designed to match terms used in different ontologies. We have implemented two methods based on linguistic similarities of terms used in the ontologies. The first looks up a dictionary or semantic network like WordNet and the second determines similarities of words based on word similarity compuoed from a domain-specific corpus of documents. We discuss our experiments that indicate that a method that uses both heuristics produces good results.

This dissertation studies representation matching: the problem of creating semantic mappings between two data representations. Examples of data representations are relational schemas, ontologies, and XML DTDs. Examples of semantic mappings include "element location of one representation maps to element address of the other", "contact-phone maps to agent-phone", and "listed-price maps to price * (1 + tax-rate)"...

One of the core challenges for the Semantic Web is the aspect of decentralization. Local structures can be modeled by ontologies. However, in order to support global communication and knowledge exchange, mechanisms have to be developed for integrating the local systems. We adopt the database approach of autonomous federated database systems and consider an architecture for federated ontologies for the Semantic Web as starting point of our work.

In a conventional information mediation scenario it is assumed that all sources, including their schemas, are known before the integrated view is defined.

: Content Management becomes a cornerstone of successful B2B electronic commerce. The B2B players use different document standards to represent their business documents, and different content standards to specify the products. Thousands of the players meet together at B2B marketplaces, and the marketplaces must be able to integrate numerous document and content standards. The large number of the standards and their significant complexity make the integration problems non-trivial and require development of special integration architecture. In the present paper we discuss the tasks and the problems which occur during the content and document integration, and survey possible solutions and available techniques. 1.

We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.