Linked Data - the story so far

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward

Glimpses of Semantic Web Technologies and Related Case Studies

Semantic web is a platform of new evolution in rapidly developing World Wide Web. Semantic web refers to extracting knowledge from large amount of data. The purpose of this paper is to give a first-hand information and description for the semantic web technology. Although several research works have been carried out in the high semantic web technology, the semantic web is yet vastly unexplored. A semantic web technological innovation is rapidly changing traditional methods of searching data and how search engines work. Few prominent semantic web case studies are presented. One of the popular applications of XML RDF is Really Simple Syndication (RSS) feed which is discussed in detail

FORGE: Enhancing elearning and research in ICT through remote experimentation

This paper presents the Forging Online Education through FIRE (FORGE) initiative, which aims to transform the Future Internet Research and Experimentation (FIRE) testbed facilities, already vital for European research, into a learning resource for higher education. From an educational perspective this project aims at promoting the notion of Self-Regulated Learning (SRL) through the use of a federation of high- performance testbeds and at building unique learning paths based on the integration of a rich linked-data ontology. Through FORGE, traditional online courses will be complemented with interactive laboratory courses. It will also allow educators to efficiently create, use and re-use FIRE-based learning experiences through our tools and techniques. And, most importantly, FORGE will enable equity of access to the latest ICT systems and tools independent of location and at low cost, strengthening the culture of online experimentation tools and remote facilities

Best Practices of Consuming Linked Open Data

The term Linked Data is defined as a set of best practices for publishing and interlinking structured data on the web. These best practices were introduced by Tim Berners-Lee and are also known as principles. These best practices are used by the vast majority of data providers leading to the establishment of a global data space known as the web of data. In this paper will analyze and explore the technical principles of Linked Data, the best practices of using Linked Data, some deployed Linked Data applications and use cases to exploit the Web of Data

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

RISK REDUCTION THROUGH TECHNOLOGICAL CONTROL OF PERSONAL INFORMATION

Abuse and harm to individuals, through harassment and bullying, coexist with Identity Theft as criminal behaviours supported by the ready availability of personal information. Incorporating privacy protection measures into software design requires a thorough understanding about how an individual's privacy is affected by Internet technologies. This research set out to incorporate such an understanding by examining privacy risks for two groups of individuals, for whom privacy was an important issue, domestic abuse survivors and teenagers. The purpose was to examine the reality of the privacy risks for these two groups. This research combined a number of approaches underpinned by a selection of foundation theories from four separate domains: software engineering; information systems; social science; and criminal behaviour. Semi-structured interviews, focus groups, workshops and questionnaires gathered information from managers of refuges and outreach workers from Women's Aid; representatives from probation and police domestic violence units; and teenagers. The findings from these first interactions provided specific examples of risks posed to the two groups. These findings demonstrated that there was a need for a selection of protection mechanisms that promoted awareness of the potential risk among vulnerable individuals. Emerging from these findings were a set of concepts that formed the basis of a novel taxonomy of threat framework designed to assist in risk assessment. To demonstrate the crossover between understanding the social environment and the use of technology, the taxonomy of threat was incorporated into a novel Vulnerability Assessment Framework, which in turn provided a basis for an extension to standard browser technology. A proof-of-concept prototype was implemented by creating an Internet Explorer 7.0 browser helper object. The prototype also made use of the Semantic Web protocols of Resource Description Framework and the Web Ontology Language for simple data storage and reasoning. The purpose of this combination was to demonstrate how the environment in which the individual primarily interacted with the Internet could be adapted to provide awareness of the potential risk, and to enable the individual to take steps to reduce that risk. Representatives of the user-groups were consulted for evaluation of the acceptability of the prototype approach. The favourable ratings given by the respondents demonstrated the acceptability of such an approach to monitoring personal information, with the provision that control remained with the individual. The evaluation exercise also demonstrated how the prototype would serve as a useful tool to make individuals aware of the dangers. The novel contribution of this research contains four facets: it advances understanding of privacy protection for the individual; illustrates an effective combination of methodology frameworks to address the complex issue of privacy; provides a framework for risk assessment through the taxonomy of threat; and demonstrates the novel vulnerability assessment framework through a proof-of-concept prototype

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites