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

Semantic wiki engines: a state of the art

Abstract. This paper is a survey about semantic wikis and the use of semantic Web technologies by wiki engines. First, it presents the main notions related to semantic wikis. Then, twelve active semantic wiki projects are compared, according to various viewpoints such as knowledge representation philosophy, use of semantic Web standards and system usability. Finally, a formal concept analysis approach is carried out to provide a guideline for the choice of semantic wiki engine, given a set of needed features. In conclusion, the paper argues about semantic wiki issues such as the weaknesses of some semantic wiki engine interoperability

Requirement Mining for Model-Based Product Design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

From Web 1.0 to Social Semantic Web: Lessons Learnt from a Migration to a Medical Semantic Wiki

International audienceOncolor is an association whose mission is to publish and share medical guidelines in oncology. As many scientific information websites built in the early times of the Internet, its website deals with unstructured data that cannot be automatically querried and is getting more and more difficult to maintain over time. The online contents access and the editing process can be improved by using web 2.0 and semantic web technologies, which allow to build collaboratively structured information bases in semantic portals. The work described in this paper aims at reporting a migration from a static HTML website to a semantic wiki in the medical domain. This approach has raised various issues that had to be addressed, such as the introduction of structured data in the unstructured imported guidelines or the linkage of content to external medical resources. An evaluation of the result by final users is also provided, and proposed solutions are discussed

Requirement mining for model-based product design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations

Requirement mining for model-based product design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate