The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation when web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the definition of ontology mapping rules by manual semantic annotation and the usage of the mapping rules and of web services for semantic queries. In order to create metadata, the framework combines the presentation layer with the data description layer in contrast to "conventional" annotation, which remains at the presentation layer. Therefore, we refer to the framework as deep annotation. t We consider deep annotation as particularly valid because, (/), web pages generated from databases outnumber static web pages, (ii), annotation of web pages may be a very intuitive way to create semantic data from a database and, (iii), data from databases should not be materialized as RDF files, it should remain where it can be handled most efficiently in its databases.

Abstract. The Semantic Web realization depends on the availability of a critical mass of metadata for the web content, associated with the respective formal knowledge about the world. We claim that the Semantic Web, at its current stage of development, is in a state of a critically need of metadata generation and usage schemata that are specific, well-defined and easy to understand. This paper introduces our vision for a holistic architecture for semantic annotation, indexing, and retrieval of documents with regard to extensive semantic repositories. A system (called KIM), implementing this concept, is presented in brief and it is used for the purposes of evaluation and demonstration. A particular schema for semantic annotation with respect to real-world entities is proposed. The underlying philosophy is that a practical semantic annotation is impossible without some particular knowledge modelling commitments. Our understanding is that a system for such semantic annotation should be based upon a simple model of real-world entity classes, complemented with extensive instance knowledge. To ensure the efficiency, ease of sharing, and reusability of the metadata,

Abstract. The process of document annotation for the Semantic Web is complex and time consuming, as it requires a great deal of manual annotation. Information extraction from texts (IE) is a technology used by some very recent systems for reducing the burden of annotation. The integration of IE systems in annotation tools is quite a new development and there is still the necessity of thinking the impact of the IE system on the whole annotation process. In this paper we initially discuss a number of requirements for the use of IE as support for annotation. Then we present and discuss a model of interaction that addresses such issues and Melita, an annotation framework that implements a methodology for active annotation for the Semantic Web based on IE. Finally we present an experiment that quantifies the gain in using IE as support to human annotators. 1.

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.

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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.

The realization of the Semantic Web requires the widespread availability of semantic annotations for existing and new documents on the Web. Semantic annotations are to tag ontology class instance data and map it into ontology classes. The fully automatic creation of semantic annotations is an unsolved problem. Instead, current systems focus on the semi-automatic creation of annotations. The Semantic Web also requires facilities for the storage of annotations and ontologies, user interfaces, access APIs, and other features to fully support annotation usage. This paper examines current Semantic Web annotation platforms that provide annotation and related services, and reviews their architecture, approaches and performance.

In this paper we propose a methodology to learn to automatically annotate domain-specific information from large repositories (e.g. Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries). Retrieved information is then used to partially annotate documents. These annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotations used to annotate more documents. It will be used to train more complex IE engines and the cycle will keep on repeating itself until the required information is obtained. The user intervention is limited to providing an initial URL and to correct information if it is the case when the computation is finished. The revised annotation can then be reused to provide further training and therefore getting more information and/or more precision.

Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, CREAM, that allows for creation of metadata. While the annotation mode of CREAM allows to create metadata for existing web pages, the authoring mode lets authors create metadata -- almost for free -- while putting together the content of a page. As a

Abstract. We present a Semantic Web application that we call CS AKTive Space. The application exploits a wide range of semantically heterogeneous and distributed content relating to Computer Science research in the UK. This content is gathered on a continuous basis using a variety of methods including harvesting and scraping as well as adopting a range models for content acquisition. The content currently comprises around five million RDF triples and we have developed storage, retrieval and maintenance methods to support its management. The content is mediated through an ontology constructed for the application domain and incorporates components from other published ontologies. CS AKTive Space supports the exploration of patterns and implications inherent in the content and exploits a variety of visualisations and multi dimensional representations. Knowledge services supported in the application include investigating communities of practice: who is working, researching or publishing with whom. This work illustrates a number of substantial challenges for the Semantic Web. These include problems of referential integrity, tractable inference and interaction support. We review our approaches to these issues and discuss relevant related work. Socio technical issues are outlined that are seen to be critical for the success or failure of our endeavour. 1.