A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management

The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprisespecific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.This work was supported in part by the Commission for Technology and Innovation (CTI) of the Swiss Confederation under Grant 14575.1 PFES-ES and the ELO Digital Office CH AG.http://www.tandfonline.com/loi/teis202018-04-30hb2017Information Scienc

Climate change and One Health

The journal The Lancet recently published a countdown on health and climate change. Attention was focused solely on humans. However, animals, including wildlife, livestock and pets, may also be impacted by climate change. Complementary to the high relevance of awareness rising for protecting humans against climate change, here we present a One Health approach, which aims at the simultaneous protection of humans, animals and the environment from climate change impacts (climate change adaptation). We postulate that integrated approaches save human and animal lives and reduce costs when compared to public and animal health sectors working separately. A One Health approach to climate change adaptation may significantly contribute to food security with emphasis on animal source foods, extensive livestock systems, particularly ruminant livestock, environmental sanitation, and steps towards regional and global integrated syndromic surveillance and response systems. The cost of outbreaks of emerging vector-borne zoonotic pathogens may be much lower if they are detected early in the vector or in livestock rather than later in humans. Therefore, integrated community-based surveillance of zoonoses is a promising avenue to reduce health effects of climate change

An ontology-based and case-based reasoning supported workplace learning approach

The support of workplace learning is increasingly relevant as the change in every form determines today’s working world in the industry and public administrations alike. Adapting quickly to a new job, a new task or a new team is a significant challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it is aligned with business goals. Our approach supports workplace learning by suggesting historical cases and providing recommendations of experts and learning resources. We utilize users’ workplace environment, we consider their learning preferences, provide them with useful prior lessons, and compare required and acquired competencies to issue the best-suited recommendations. Our research work follows a Design Science Research strategy and is part of the European funded project Learn PAd. The recommender system introduced here is evaluated in an iterative manner, first by comparing it to previously elicited user requirements and then through practical application in a test process conducted by the project application partner.http://www.springer.comseries/78992018-09-10hj2017Informatic

KPIs 4 workplace learning

Enterprises and Public Administrations alike need to ensure that newly hired employees are able to learn the ropes fast. Employers also need to support continuous workplace learning. Workplace learning should be strongly related to business goals and thus, learning goals should directly add to business goals. To measure achievement of both learning and business goals we propose augmented Key Performance Indicators (KPI). In our research we applied model driven engineering. Hence we developed a model for a Learning Scorecard comprising of business and learning goals and their KPIs represented in an ontology. KPI performance values and scores are calculated with formal rules based on the SPARQL Inferencing Notation. Results are presented in a dashboard on an individual level as well as on a team/group level. Requirements, goals and KPIs as well as performance measurement were defined in close cooperation with Marche Region, business partner in Learn PAd

Semantics for the Wiki - Final Iteration

Deliverable D5.5 focuses on extensions of the Wiki that exploit the knowledge stored in the Learn PAd ontology in order to provide broader navigation possibilities and personalisation options to learners. Navigation is enhanced by injecting entry points and context menus into the text displayed on Wiki pages. Regardless of whether the text is exported from models or contributed by Learn PAd users, it will be analysed in order to identify mentions of ontology concepts of certain type - persons, links to documents and organisational units. Such mentions are highlighted and equipped with a context menu that learners can use to navigate the ontology. That is, they can choose among various options to navigate to representations of other ontology concepts that are related to the one mentioned in the text that they are currently reading. Thus, from within the context of their learning (i.e. while reading a Wiki page), they can traverse relevant parts of the Learn PAd ontology to get broader insights. Further, personalisation concepts are also based on the recognition of ontological concepts and on being able to bookmark them. Learners can decide to create an association between a bookmarked entity and the current context in which they encountered it. This leads to a process-oriented categorization of bookmarks that - when reviewed later by that person - will support process-based learning. We finally report on an extension of earlier work, namely the concretisation of the concept of retrofitting civil servants\u27 contributions to Learn PAd models

Semantics for the Wiki - First Iteration

Deliverable D5.2 focuses on Ontology Supported Learning. We analysed and partially enhanced already defined requirements and derived additional ones from field research. Recommendations should comprise (contact information of) experts who can mentor a learner, wiki articles that correspond one-to-one to model elements (regarded as learning objects related to these model elements) and, learning material like textbooks but also audio and video documents. Recommendations of experts, learning objects and learning material are provided in a context-sensitive and personalized manner. This is possible as the Learn PAd ontology not only represents characteristics of the wiki content (and hence, the models), but also profiles of the learners, containing their competencies and learning preferences. In addition - at run-time - process execution data and application data are analysed for recommendations. Recommendations can be made in all of three modes of learning supported by the Learn PAd system, i.e. browsing, simulation and execution. The more is known about the context of a learner the more sophisticated the recommendations. For the research reported in D5.2 we adopted the design science research methodology for information systems complemented by the posing competency questions for ontology engineering and evaluation. For the research reported in D5.2 we adopted the design science research methodology for information systems complemented by posing competency questions for ontology engineering and evaluation. In order to elicit realistic requirements and to make sure that the solution can be applied in practice, we created an application scenario together with Marche Region that reflects a learner\u27s real working environment and illustrates role and output of the recommendation system. The main artefact reported on in this deliverable is the recommender system specification. We assessed the first iteration of the recommender system by comparing the specified behaviour with the requirements. In addition, recommendations provided for learning within the browsing and simulation mode are evaluated within D8.2. Based on the specification the recommendation system is currently being developed. D5.2 provides the technical specification for it

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present