AceWiki: A Natural and Expressive Semantic Wiki

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.Comment: To be published as: Proceedings of Semantic Web User Interaction at CHI 2008: Exploring HCI Challenges, CEUR Workshop Proceeding

How Controlled English can Improve Semantic Wikis

The motivation of semantic wikis is to make acquisition, maintenance, and mining of formal knowledge simpler, faster, and more flexible. However, most existing semantic wikis have a very technical interface and are restricted to a relatively low level of expressivity. In this paper, we explain how AceWiki uses controlled English - concretely Attempto Controlled English (ACE) - to provide a natural and intuitive interface while supporting a high degree of expressivity. We introduce recent improvements of the AceWiki system and user studies that indicate that AceWiki is usable and useful

A Formal Context Representation Framework for Network-Enabled Cognition

Network-accessible resources are inherently contextual with respect to the specific situations (e.g., location and default assumptions) in which they are used. Therefore, the explicit conceptualization and representation of contexts is required to address a number of problems in Network- Enabled Cognition (NEC). We propose a context representation framework to address the computational specification of contexts. Our focus is on developing a formal model of context for the unambiguous and effective delivery of data and knowledge, in particular, for enabling forms of automated inference that address contextual differences between agents in a distributed network environment. We identify several components for the conceptualization of contexts within the context representation framework. These include jurisdictions (which can be used to interpret contextual data), semantic assumptions (which highlight the meaning of data), provenance information and inter-context relationships. Finally, we demonstrate the application of the context representation framework in a collaborative military coalition planning scenario. We show how the framework can be used to support the representation of plan-relevant contextual information

AceWiki: Collaborative Ontology Management in Controlled Natural Language

AceWiki is a prototype that shows how a semantic wiki using controlled natural language - Attempto Controlled English (ACE) in our case - can make ontology management easy for everybody. Sentences in ACE can automatically be translated into first-order logic, OWL, or SWRL. AceWiki integrates the OWL reasoner Pellet and ensures that the ontology is always consistent. Previous results have shown that people with no background in logic are able to add formal knowledge to AceWiki without being instructed or trained in advance

Mind the Gap: From Desktop to App

In this article we present a new mobile game, edugames4all MicrobeQuest!, that covers core learning objectives from the European curriculum on microbe transmission, food and hand hygiene, and responsible antibiotic use. The game is aimed at 9 to 12 year olds and it is based on the desktop version of the edugames4all platform games. We discuss the challenges and lessons learned transitioning from a desktop based game to a mobile app. We also present the seamless evaluation obtained by integrating the assessment of educa- tional impact of the game into the game mechanics

FEMwiki: crowdsourcing semantic taxonomy and wiki input to domain experts while keeping editorial control: Mission Possible!

Highly specialized professional communities of practice (CoP) inevitably need to operate across geographically dispersed area - members frequently need to interact and share professional content. Crowdsourcing using wiki platforms provides a novel way for a professional community to share ideas and collaborate on content creation, curation, maintenance and sharing. This is the aim of the Field Epidemiological Manual wiki (FEMwiki) project enabling online collaborative content sharing and interaction for field epidemiologists around a growing training wiki resource. However, while user contributions are the driving force for content creation, any medical information resource needs to keep editorial control and quality assurance. This requirement is typically in conflict with community-driven Web 2.0 content creation. However, to maximize the opportunities for the network of epidemiologists actively editing the wiki content while keeping quality and editorial control, a novel structure was developed to encourage crowdsourcing – a support for dual versioning for each wiki page enabling maintenance of expertreviewed pages in parallel with user-updated versions, and a clear navigation between the related versions. Secondly, the training wiki content needs to be organized in a semantically-enhanced taxonomical navigation structure enabling domain experts to find information on a growing site easily. This also provides an ideal opportunity for crowdsourcing. We developed a user-editable collaborative interface crowdsourcing the taxonomy live maintenance to the community of field epidemiologists by embedding the taxonomy in a training wiki platform and generating the semantic navigation hierarchy on the fly. Launched in 2010, FEMwiki is a real world service supporting field epidemiologists in Europe and worldwide. The crowdsourcing success was evaluated by assessing the number and type of changes made by the professional network of epidemiologists over several months and demonstrated that crowdsourcing encourages user to edit existing and create new content and also leads to expansion of the domain taxonomy

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform