Wikipedia is the world's largest collaboratively edited source of encyclopaedic knowledge. But in spite of its utility, its contents are barely machine-interpretable. Structural knowledge, e. g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data is only available as plain text and thus can not be processed by its actual meaning. We provide

Abstract Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used. 1

Semantic wikis combine the collaborative environment of a classical wiki with features of semantic technologies. Semantic data is used to structure information in the wiki, to improve information access by intelligent search and navigation, and to enable knowledge exchange across applications. Though semantic wikis hardly support complex semantic knowledge and inferencing, we argue that this is not due to a lack of practical use cases. We discuss various tasks for which advanced reasoning is desirable, and identify open challenges for the development of inferencing tools and formalisms. Our goal is to outline concrete options for overcoming current problems, since we believe that many problems in semantic wikis are prototypical for other Semantic Web applications as well. Throughout the paper, we refer to our semantic wiki implementations IkeWiki and Semantic MediaWiki for practical illustration.

Abstract. This article introduces KWQL, spoken “quickel”, a rulebased query language for a semantic wiki based on the label-keyword query paradigm. KWQL allows for rich combined queries of full text, document structure, and informal to formal semantic annotations. It offers support for continuous queries, that is, queries re-evaluated upon updates to the wiki. KWQL is not restricted to data selection, but also offers database-like views, enabling “construction”, the re-shaping of the selected (meta-)data into new (meta-)data. Such views amount to rules that provide a convenient basis for an admittedly simple, yet remarkably powerful form of reasoning. KWQL queries range from simple lists of keywords or label-keyword pairs to conjunctions, disjunctions, or negations of queries. Thus, queries range from elementary and relatively unspecific to complex and fully specified (meta-)data selections. Consequently, in keeping with the “wiki way”, KWQL has a low entry barrier, allowing casual users to easily locate and retrieve relevant data, while letting advanced users make use of its full power. 1

Abstract. As more and more websites start to embed RDFa content in their web application view, the need arises to provide a more extensive way for viewing and editing this semantic content independently from the remainder of the application. We present a JavaScript API that allows the independent creation of editing widgets for embedded RDFa, which adds a new edge to Web development in the context of the Semantic Web. As an addition, the API also provides sound update methods that allow on-the-spot model synchronization between client and server. 1

This paper details the main concepts and the architecture of UfoWiki, a semantic wiki farm – i.e. a server of wikis – that uses form-based templates to produce ontology-based knowledge. Moreover, the system allows different wikis to share and interlink ontology instance between each other, so that knowledge can be produced by different and distinct communities in a distributed but collaborative way.

The fast growth and spread of Web 2.0 environments have demonstrated the great willingness of general Web users to contribute and share various type of content and information. Many very successful web sites currently exist which thrive on the wisdom of the crowd, where web users in general are the sole data providers and curators. The Semantic Web calls for knowledge to be semantically represented using ontologies to allow for better access and sharing of data. However, constructing ontologies collaboratively is not well supported by most existing ontology and knowledge-base editing tools. This has resulted in the recent emergence of a new range of collaborative ontology construction tools with the aim of integrating some Web 2.0 features into the process of structured knowledge construction. This paper provides a survey of the start of the art of these tools, and highlights their significant features and capabilities. 1.

A lot of functionality is needed when an application, such as a museum cataloguing system, is extended with semantic capabilities, for example ontological indexing functionality or multi-facet search. To avoid duplicate work and to enable easy and cost-efficient integration of information systems with the Semantic Web, we propose a web widget approach. Here, data sources are combined with functionality into readyto-use software components that allow adding semantic functionality to systems with just a few lines of code. As a proof of the concept, we present a collection of general semantic web widgets and case applications that use them, such as the ontology server ONKI, the annotation editor SAHA and the culture portal CultureSampo.

WWW home page:http://ecs.soton.ac.uk

Abstract. The performance of triple stores is one of the major obstacles for the deployment of semantic technologies in many usage scenarios. In particular Semantic Web applications, which use triple stores as persistence backends, trade performance in for the advantage of flexibility with regard to information structuring. In order to get closer to the performance of relational database-backed Web applications, we developed an approach for improving the performance of triple stores by caching query results and even complete application objects. The selective invalidation of cache objects, following updates of the underlying knowledge bases, is based on analysing the graph patterns of cached SPARQL queries in order to obtain information which updates will change the query result. We evaluated our approach by extending the BSBM triple store benchmark with an update dimension as well as in typical Semantic Web application scenarios. 1