We present OntoWiki, a tool providing support for agile, distributed knowledge engineering scenarios. OntoWiki facilitates the visual presentation of a knowledge base as an information map, with different views on instance data. It enables intuitive authoring of semantic content, with an inline editing mode for editing RDF content, similar to WYSIWYG for text documents. It fosters social collaboration aspects by keeping track of changes, allowing to comment and discuss every single part of a knowledge base, enabling to rate and measure the popularity of content and honoring the activity of users. Ontowiki enhances the browsing and retrieval by offering semantic enhanced search strategies.

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of Information Retrieval on the Semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search.

This article presents the semantic portal MUSEUMFINLAND for publishing heterogeneous museum collections on the Semantic Web. It is shown how museums with their semantically rich and interrelated collection content can create a large, consolidated semantic collection portal together on the web. By sharing a set of ontologies, it is possible to make collections semantically interoperable, and provide the museum visitors with intelligent content-based search and browsing services to the global collection base. The architecture underlying MUSEUMFINLAND separates generic search and browsing services from the underlying application dependent schemas and metadata by a layer of logical rules. As a result, the portal creation framework and software developed has been applied successfully to other domains as well. MUSEUMFINLAND got the Semantic Web Challence Award (second prize) in 2004.

Abstract. Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontologybased KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion. 1

This paper discusses the creation of terminologies, ontologies, and annotations when publishing semantic web content. The problem is approached by presenting the content creation processes of the semantic portal MUSEUMFINLAND that is intended for publishing collections of Finnish museums on the web.

This paper presents a case-study of converting the content of an existing eLearning portal based on the Learning Object Metadata (LOM) format into a semantic portal. It is shown how the content can be provided for the end-user from different semantic perspectives (views) based on a set of ontologies whose concepts are extracted from the original metadata. A semantic recommender system based on the same ontologies and metadata enhances the system usability further. This recommender system links the learning objects of the portal with each other with short labels that explain to the end-user the reason for the links, which is especially important in the eLearning domain. Furthermore, the system links the material also with the content of another cultural semantic portal, providing interportal semantic links. The application, consisting of some 2200 video and audio clips as learning objects, is available on the web where it can be compared with the original portal.

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We introduce the concept of community-driven ontology management and demonstrate the added value to conventional ontology management of being community-driven. Further, we present an implementation of an infrastructure supporting community-driven ontology management. The implemented infrastructure was deployed as a part of the intranet at DERI – Digital Enterprise Research Institute, and the community’s response and behavior were observed. The results obtained prove feasibility and advantages of community-driven ontology management. 1.

This paper shows how semantic web techniques can be applied to solving problems of distributed content creation, discovery, linking, aggregation, and reuse in health information portals, both from end-users ’ and content publishers’ viewpoints. As a case study, the national semantic health portal HEALTH-FINLAND is presented. It provides citizens with intelligent searching and browsing services to reliable and up-to-date health information created by various health organizations in Finland. The system is based on a shared semantic metadata schema, ontologies, and ontology services. The content includes metadata about thousands of web documents such as web pages, articles, reports, campaign information, news, services, and other information related to health.

Abstract. The focus of contemporary Web information retrieval systems has been to provide efficient support for the querying and retrieval of relevant documents. More recently, information retrieval over semantic metadata extracted from the Web has received an increasing amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this metadata is an interesting and challenging research topic. Just as ranking of documents is a critical component of today’s search engines, the ranking of complex relationships will be an important component in tomorrow’s Semantic Web analytics engines. Building upon our recent work on specifying and discovering complex relationships in RDF data, called Semantic Associations, we present a flexible ranking approach which can be used to identify more interesting and relevant relationships in the Semantic Web. Additionally, we demonstrate our ranking scheme’s effectiveness through an empirical evaluation over a real-world dataset. Keywords. H.3.3.d Metadata, H.3.5.f XML/XSL/RDF 1