OpenCourseWare (OCW) has become a desirable source for sharing free educational resources which means there will always be users with differing needs. It is therefore the responsibility of OCW platform developers to consider accessibility as one of their prioritized requirements to ensure ease of use for all, including those with disabilities. However, the main challenge when creating an accessible platform is the ability to address all the different types of barriers that might affect those with a wide range of physical, sensory and cognitive impairments. This article discusses accessibility and personalization strategies and their realisation in the SlideWiki platform, in order to facilitate the development of accessible OCW. Previously, accessibility was seen as a complementary feature that can be tackled in the implementation phase. However, a meaningful integration of accessibility features requires thoughtful consideration during all project phases with active involvement of related stakeholders. The evaluation results and lessons learned from the SlideWiki development process have the potential to assist in the development of other systems that aim for an inclusive approach.

Inflammatory bowel disease (IBD) is a chronic disease characterized by numerous, hard to predict periods of relapse and remission. "Digital twin" approaches, leveraging personalized predictive models, would significantly enhance therapeutic decision-making and cost-effectiveness. However, the associated computational and statistical methods require high quality data from a large population of patients. Such a comprehensive repository is very challenging to build, though, and none is available for IBD. To overcome this, a promising approach is to employ a knowledge graph, which is built from the available data and would help predicting IBD episodes and delivering more relevant personalized therapy at the lowest cost. In this research, we present a knowledge graph developed on the basis of patient records which are collected from one of the largest German gastroentologic outpatient clinic. First, we designed IBD ontology that encompasses the vocabulary, specifications and characteristics associated by physicians with IBD patients, such as disease classification schemas (e.g., Montreal Classification of IBD), status of the disease activity, and medications. Next, we defined the mappings between ontology entities and database variables. Physicians and project members participating in the Fraunhofer MED2ICIN project, validated the ontology and the knowledge graph. Furthermore, the knowledge graph has been validated against the competency questions compiled by physicians.

Over the years, the demand for interoperability support between diverse applications has significantly in- creased. The Resource Definition Framework (RDF), among other solutions, is utilized as a data modeling language which allows for encoding the knowledge from various domains in a unified representation. More- over, a vast amount of data from heterogeneous data sources are continuously published in documents using the RDF format. Therefore, these RDF documents should be syntactically correct in order to enable software agents performing further processing. Albeit, a number of approaches have been proposed for ensuring error-free RDF documents, commonly they are not able to identify all syntax errors at once by failing on the first encountered error. In this paper, we tackle the problem of simultaneous error identification, and propose RDF-Doctor, a holistic approach for detecting and resolving syntactic errors in a semi-automatic fashion. First, we define a comprehensive list of errors that can be detected along with customized error messages to allow users for a better understanding of the actual errors. Next, a subset of syntactic errors is corrected automatically based on matching them with predefined error messages. Finally, for a particular number of errors, customized and meaningful messages are delivered to users to facilitate the manual corrections process. The results from empirical evaluations provide evidence that the presented approach is able to effectively detect a wide range of syntax errors and automatically correct a large subset of them.

In the era of globalization, it is strongly recommended for a company when developing a new project to identify companies (at local and international levels) that have already developed similar projects to reuse their experience and reproduce their findings. It should be noticed that the reuse is an eternal grail to increase projects productivity. It may concern all aspects of the life cycle of project development that ranges from user requirements to codes. In this study, we focus on user requirements. In such a context, each company (site) has already expressed its own requirements using different modeling formalisms, conceptualizations and vocabularies and scheduling of tasks. This situation makes the reuse and exploitation of the requirements harder, since the heterogeneity is everywhere. To reduce this heterogeneity, the most important studies assume the existence of pivot models and shared ontologies that allow reducing heterogeneities. Unfortunately, these hypotheses are out of phase with the reality, in which the sites are autonomous and usually multilingual. In this paper, we propose to relax these hypotheses. Firstly, we propose a framework for requirements integration using an ontology-based matching system that identifies and unifies different heterogeneous items. Secondly, a reasoning mechanism is proposed to deduce relationships between integrated requirements. Finally, our proposed framework is evaluated w.r.t its feasibility, results quality and effectiveness. A tool, called MURGROOM implementing the framework is also proposed.

Due to the increasing number of information sources available on the web and their distribution and heterogeneity, ontology alignment became a very important and inevitable problem to resolve in order to ensure semantic interoperability between these sources. Instance-based ontology alignment represents a very promising technique to find semantic correspondences between entities of different ontologies. In practice, two situations may arise: ontologies that share common instances and those share few or do not share common instances. In this paper, the authors describe a new approach to manage the latter case. This approach exploits the reasoning on ontologies in order to create a corpus of common instances. They have used the Biblio and Finance tests of Benchmark series of the OAEI 2012 evaluation campaign to evaluate the performance of their approach. The results obtained show the good performance of the authors' approach compared to ontology alignment systems and improves significantly the instance-based and reasoning-based methods.