A linked data-driven & service-oriented architecture for sharing educational resources

The two fundamental aims of managing educational resources are to enable resources to be reusable and interoperable and to enable Web-scale sharing of resources across learning communities. Currently, a variety of approaches have been proposed to expose and manage educational resources and their metadata on the Web. These are usually based on heterogeneous metadata standards and schemas, such as IEEE LOM or ADL SCORM, and diverse repository interfaces such as OAI-PMH or SQI. Also, there is still a lack of usage of controlled vocabularies and available data sets that could replace the widespread use of unstructured text for describing resources. On the other hand, the Linked Data approach has proven that it offers a set of successful principles that have the potential to alleviate the aforementioned issues. In this paper, we introduce an architecture and prototype which is fundamentally based on (a) Linked Data principles and (b) Service-orientation to resolve the integration issues for sharing educational resources

Linked education: interlinking educational resources and the web of data

Research on interoperability of technology-enhanced learning (TEL) repositories throughout the last decade has led to a fragmented landscape of competing approaches, such as metadata schemas and interface mechanisms. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web and offers a large potential to solve interoperability issues in the field of TEL. In this paper, we describe a general approach to exploit the wealth of already existing TEL data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. This approach has been implemented in the context of the mEducator project where data from a number of open TEL data repositories has been integrated, exposed and enriched by following Linked Data principles

Linking with Meaning: Ontological Hypertext for Scholars

The links in ontological hypermedia are defined according to the relationships between real-world objects. An ontology that models the significant objects in a scholar’s world can be used toward producing a consistently interlinked research literature. Currently the papers that are available online are mainly divided between subject- and publisher-specific archives, with little or no interoperability. This paper addresses the issue of ontological interlinking, presenting two experimental systems whose hypertext links embody ontologies based on the activities of researchers and scholars

Selected papers from the 15th Annual Bio-Ontologies special interest group meeting

© 2013 Soldatova et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Over the 15 years, the Bio-Ontologies SIG at ISMB has provided a forum for discussion of the latest and most innovative research in the bio-ontologies development, its applications to biomedicine and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. The seven papers and the commentary selected for this supplement span a wide range of topics including: web-based querying over multiple ontologies, integration of data, annotating patent records, NCBO Web services, ontology developments for probabilistic reasoning and for physiological processes, and analysis of the progress of annotation and structural GO changes

A Survey of Semantic Metadata Management Models for the Social Web

12Social systems are a new generation of Web 2.0 applications, characterized by their primarily user-driven content and the ability to mediate personal and social information across communities, such as teams, communities, and organizations. The recent growth and adaptation of social systems for personal and social information management has created new opportunities for users to be producers as well as consumers of information. This paper aims at studying the different models that have been proposed to better connect resources, annotations and users and their usage in the social Web. The paper aims to answer questions like: \textit{What are the existing models that allow to semantically describe resources, users and tags in the social Web? What are the characteristics of such models? What are the differences between those models?} The final objective is to provide an understandable study and comparison of some of the existing models to help researchers, and developers, to make their decision whenever there is a need to use a semantic meta-data model in the social Web. More concretely, this work aims to be a reference guide for different professionals in order to accelerate the adoption of such technologies in the Social Web

Representing and coding the knowledge embedded in texts of Health Science Web published articles

Despite the fact that electronic publishing is a common activity to scholars electronic journals are still based in the print model and do not take full advantage of the facilities offered by the Semantic Web environment. This is a report of the results of a research project with the aim of investigating the possibilities of electronic publishing journal articles both as text for human reading and in machine readable format recording the new knowledge contained in the article. This knowledge is identified with the scientific methodology elements such as problem, methodology, hypothesis, results, and conclusions. A model integrating all those elements is proposed which makes explicit and records the knowledge embedded in the text of scientific articles as an ontology. Knowledge thus represented enables its processing by intelligent software agents The proposed model aims to take advantage of these facilities enabling semantic retrieval and validation of the knowledge contained in articles. To validate and enhance the model a set of electronic journal articles were analyzed

SIGHTED: A Framework for Semantic Integration of Heterogeneous Sensor Data on the Internet of Things

AbstractSensors are embedded nowadays in a growing number of everyday life objects. Smartphones, wearables, and sensor networks together play an important role in bridging the gap between physical and cyber worlds, a fundamental aspect of the Internet of Things vision. The ability to reuse sensor data integrated from multiple heterogeneous sources is a step towards building innovative applications and services. In this paper SIGHTED, a sensor data integration framework, is proposed exploiting semantic web technologies and linked data principles. It provides a layered structure as a guideline for integrating sensor data from various sources supporting accessibility and usability. DotThing, a demo platform, is implemented as an instantiation of SIGHTED framework and evaluated. Smartphones and sensor nodes are connected to DotThing showing the ability to query and reuse integrated sensor data from multiple sources to create more flexible horizontal applications. DotThing implementation also demonstrates the need for adding a semantic layer to existing IoT cloud-based platforms, like Xively, that generally lack such layer resulting in proprietary vertical solutions with limited data integration and discovery capabilities. DotThing makes use of vocabularies from existing ontologies on the linked data cloud providing a unified model to annotate data and link it to existing resources on the web

Enabling smart learning systems within smart cities using open data

Deploying ad-hoc learning environments to use and represent data from multiple sources and networks and to dynamically respond to user demands could be very expensive and ineffective in the long run. Moreover, most of the available data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards. It is preferable to focus on data availability to choose and develop interoperability strategies suitable for smart learning systems based on open standards and allowing seamless integration of third-party data and custom applications. This paper highlights the opportunity to take advantage of emerging technologies, like the linked open data platforms and automatic reasoning to effectively handle the vast amount of information and to use data linked queries in the domain of cognitive smart learning systems