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

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.

Vocabularies that provide unique identifiers for conceptual elements of a domain can improve precision and recall in knowledge-management applications. Although creating and maintaining such vocabularies is generally hard, wiki users easily manage to develop comprehensive, informal definitions of terms, each one identified by a URI. Here, the authors show that the URIs of Wikipedia entries are reliable identifiers for conceptual entities. They also demonstrate how Wikipedia entries can be used for annotating Web resources and knowledge assets and give precise estimates of the amount of Wikipedia URIs in terms of the popular Proton ontology’s top-level concepts. Knowledge management aims to help organizations and individuals better exploit their intellectual assets — particularly by reusing previous experiences and improving access to knowledge distributed over multiple human actors, systems, and other resources. Retrieving relevant assets can be difficult because the conceptual specificity of terms in a search task is frequently very high. Also, an organization’s most valuable assets often occupy areas with high conceptual dynamics due to innovation, which means it must be possible to add novel elements to the vocabulary in a timely manner. Ontologies — consensual, explicit conceptualizations of a domain of discourse 1–3 — are a candidate technology for improving precision and recall in knowledge management. Unfortunately, potential adopters of ontology-based solutions face a severe shortage of current, high-quality ontologies for many domains. Many ontologies published on the Web are outdated, “dead ” collections that single individuals created in some academic research context. One potential explanation for this is that creating and maintaining an ontology requires specific tools and skills, which domain experts frequently lack. In contrast, wikis make it very simple for individuals to create new entries or to modify existing ones, and

Abstract: The paper presents YAWN, a system to convert the well-known and widely used Wikipedia collection into an XML corpus with semantically rich, self-explaining tags. We introduce algorithms to annotate pages and links with concepts from the WordNet thesaurus. This annotation process exploits categorical information in Wikipedia, which is a high-quality, manually assigned source of information, extracts additional information from lists, and utilizes the invocations of templates with named parameters. We give examples how such annotations can be exploited for high-precision queries.

We present AceWiki, a prototype of a new kind of semantic wiki using the controlled natural language Attempto Controlled English (ACE) for representing its content. ACE is a subset of English with a restricted grammar and a formal semantics. The use of ACE has two important advantages over existing semantic wikis. First, we can improve the usability and achieve a shallow learning curve. Second, ACE is more expressive than the formal languages of existing semantic wikis. Our evaluation shows that people who are not familiar with the formal foundations of the Semantic Web are able to deal with AceWiki after a very short learning phase and without the help of an expert.

Abstract. Wikis are successful tools for collaborative information collection. Wikis are becoming popular knowledge management tools, but do not fully support the requirements for such tools, namely structured search and knowledge reuse. Adding semantic annotations to Wikis helps to address these limitations by offering advanced information access (navigation and querying) and allowing knowledge reuse (through embedded queries and semantic information exchange). We present an architecture for Semantic Wikis and present our prototype SemperWiki. 1

Wikis are popular collaborative hypertext authoring environments, but they neither support structured access nor information reuse. Adding semantic annotations helps to address these limitations. We present an architecture for Semantic Wikis and discuss design decisions including structured access, views, and annotation language. We present our prototype SemperWiki that implements this architecture.

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—In this paper, we introduce Noosphere Networked Entry eXtension and Unification System (NNexus), a generalization of the automatic linking engine of Noosphere (at PlanetMath.org) and the first system that automates the process of linking disparate “encyclopedia ” entries into a fully connected conceptual network. The main challenges of this problem space include: 1) linking quality (correctly identifying which terms to link and which entry to link to with minimal effort on the part of users), 2) efficiency and scalability, and 3) generalization to multiple knowledge bases and web-based information environment. We present the NNexus approach that utilizes subject classification and other metadata to address these challenges. We also present evaluation results demonstrating the effectiveness and efficiency of the approach and discuss ongoing and future directions of research. Index Terms—E-learning, automatic linking, wiki, Semantic Web. Ç

Abstract. The merging of Wikis and the Semantic Web leads to the possibility of editable Web pages where users can create, change and store knowledge. This generated knowledge is usable to enhance the way conventional Wikis organize, retrieve and present content. However, Web-based information is increasingly non-textual. While semantic Wikis are expected to provide a powerful path to the public Semantic Web, multimedia content support is already poor in their non-semantic counterparts. This paper approaches how collaborative multimedia information management can be realised by unifying principles from Wiki, Semantic Web and multimedia research. As a result we introduce the Makna semantic Wiki and propose an extension to integrate multimedia support, entitled MultiMakna. 1.