An information system like the Web is a continuously evolving system consisting of multiple heterogeneous information sources, covering a wide domain of discourse, and a huge number of users (human or software) with diverse characteristics and needs, that produce and consume information. The challenge nowadays is to build a scalable information infrastructure enabling the effective, accurate, content-\Delta retrieval of information, in a way that adapts to the characteristics and interests of the users.

In this paper we explain how merging of ontologies is captured by the pushout construction from category theory, and argue that this is a very natural approach to the problem. We study this independent of a specific choice of ontology representation

Ontologies are becoming increasingly more important in many different areas, including the knowledge management area. In knowledge management, ontologies can be used as an instrument to make knowledge assets intelligently accessible to people in organizations through an intranet or the Internet. Most enterprises agree that knowledge is an essential asset for success and survival on a increasingly competitive and global market. In this paper, we present an ontology-based approach through a large-scale initiative involving knowledge management for the knowledge-acquisition research community.

Abstract. This paper elaborates on scenarios for collaborative knowledge creation in the spirit of the trialogical learning paradigm. According to these scenarios the group knowledge base is formed by combining the knowledge bases of the participants according to various methods. The provision of flexible methods for defining various aspects of the group knowledge is expected to enhance synergy in the knowledge creation process and could lead to the development of tools that overcome the inelasticities of the current knowledge creation practices. Subsequently, these scenarios are projected to various knowledge representation frameworks and for each one of them the paper analyzes and discusses related techniques and identifies issues that are worth further research. 1

Abstract. A faceted taxonomy is a set of taxonomies, each describing a given domain from a different aspect, or facet. The indexing of domain objects is done through conjunctive combinations of terms from the facets, called compound terms. A faceted taxonomy has several advantages over a single hierarchy of terms, including conceptual clarity, compactness and scalability. A drawback, however, is the cost of avoiding invalid combinations, i.e. compound terms that do not apply to any object in the domain. This need arises in both indexing and retrieval, and typically involves human effort for specifying the valid compound terms one by one. We here propose a compound term composition algebra which can be used to generate valid compound terms in a given faceted taxonomy in an efficient and flexible manner. It works on the basis of the original simple terms of the facets and a small set of positive and/or negative statements. In each algebraic operation, we adopt a closed-world assumption with respect to the declared positive or negative statements. The taxonomy algebra can be exploited in dynamically generating navigation trees, a significant browsing aid. 1

The NCRI Informatics Initiative has been established with the goal of using informatics to maximise the impact of cancer research. A clear foundation to achieving this goal is to enable the development of an informatics platform in the UK that facilitates access to, and movement of, data generated from research funded by NCRI Partner organisations, across the spectrum from genomics to clinical trials. To assure the success of such a system, an initial project has been defined to establish and document the requirements for the platform and to construct and validate the key information models around which the platform will be built. The platform will need to leverage many projects, tools and resources including those generated by many e-Science projects. It also required contributing to the development of a global platform through a close interaction with similar efforts being developed by the NCI in the USA. This paper recounts our experience in analysing the requirements for the platform, and explains the customised analysis approach and techniques utilised in the project. 1.

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. To achieve 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 rst to begin exploring these issues. In this paper, we describe our experiences developing and using the SHOE language. We beginby describing the unique features of the World Wide Web and how they must in uence potential Semantic Web languages. We then discuss why web standards such as XML and RDF are currently insu cient for the Semantic Web. We present SHOE, a language which allows web pages to be annotated with semantics, describe its syntax and semantics, and discuss our approaches to handling characteristics of the Web such as distributed authority and rapid evolution. We discuss the implementation issues of such a language, and describe some generic tools that we have built to aid in its use. Finally, we demonstrate the language and tools by applying them to two di erent domains. The language, tools, and details of the applications are all available on the World Wide Web at

Definition of a common framework for characterizing alignment of heterogeneous information. This report describes various approaches towards this goal and shows the relations between them. It also provides a description of the alignment sructure and process.

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.

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