Supporting contextualized learning with linked open data

This paper proposes a template-based approach to semi-automatically create contextualized learning tasks out of several sources from the Web of Data. The contextualization of learning tasks opens the possibility of bridging formal learning that happens in a classroom, and informal learning that happens in other physical spaces, such as squares or historical buildings. The tasks created cover different cognitive levels and are contextualized by their location and the topics covered. We applied this approach to the domain of History of Art in the Spanish region of Castile and Leon. We gathered data from DBpedia, Wikidata and the Open Data published by the regional government and we applied 32 templates to obtain 16K learning tasks. An evaluation with 8 teachers shows that teachers would accept their students to carry out the tasks generated. Teachers also considered that the 85% of the tasks generated are aligned with the content taught in the classroom and were found to be relevant to learn in other informal spaces. The tasks created are available at https://casuallearn.gsic.uva.es/sparql.     

Dimensional enrichment of statistical linked open data

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.     

Scientific data integration system in the linked open data space

The paper is concerned with a project of data integration system designed for work in the Linked Open Data space. A system architecture is suggested, and basic principles of its functioning are discussed. For data integration, it is proposed to use a combined approach based on Linked Data sets. The system can be applied for integration of data from numerous autonomous sources, between which sufficiently stable links may be identified.     

Progressor: social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students.     

Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural modelling

Innovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions.     

Brand interactions and social media: Enhancing user loyalty through social networking sites

This paper aims to investigate how user loyalty can be achieved and maintained through social networking sites. More specifically, we intend to test the relationships between brands, user loyalty and social media. The research thus provides insights into user-brand relationships through social media and argues how loyal customers can be through social networking websites. Although there are considerable numbers of studies about loyalty; there exists very limited work studying user loyalty through social networking websites. This research presents clearly the reasons for engaging with brands online and examines user behaviors and loyalty. Research provided strong evidence that majority of the social network users follow brand fan pages via social media, even though they have different reasons to do so. The study also measures users’ behavioral and attitudinal loyalty behaviors. Their level of trust to the information they obtained about brands through social media is also established. The hypotheses tested show that brands and customer satisfaction are both positively related to users’ behavioral loyalty.     

TWC LOGD: A portal for linked open government data ecosystems

International open government initiatives are releasing an increasing volume of raw government datasets directly to citizens via the Web. The transparency resulting from these releases not only creates new application opportunities but also imposes new burdens inherent to large-scale distributed data integration, collaborative data manipulation and transparent data consumption. The Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI) has developed the Semantic Web-based TWC LOGD portal to support the deployment of linked open government data (LOGD). The portal is both an open source infrastructure supporting linked open government data production and consumption and a vibrant community portal that educates and serves the growing international open government community of developers, data curators and end users. This paper motivates and introduces the TWC LOGD Portal and highlights innovative aspects and lessons learned.     

基于社交网络和关联数据的服务网络构建方法

网络中可用服务的急剧增加对面向服务计算技术的发展起到了极大的推动作用。针对服务的规模和利用率远没有达到预期,以及服务之间交互关系的复杂性问题,提出基于社交网络和关联数据的服务网络构建方法。首先,结合社交网络和关联数据方法提出服务网络（SN）的概念,以提高服务的规模及利用率;其次,为了提升服务发现的可行性及有效性,构建服务网络中的服务社区;然后,形式化地表述服务网络中服务之间的关联关系和属性,以解决服务之间互操作问题;最后,结合旅游服务网络案例研究分析所提出的方法。研究结果表明,基于社交网络和关联数据的方法可有效解决网络服务利用率低以及服务关系复杂性的问题。     

Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data

When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings and they lack the capability to generate profits with value-added services. To tackle these two issues, we have built and evaluated a collaborative filtering recommender system which (a) makes individualized recommendations of potentially interesting value-added services when customers express interest in a particular physical product and also (b) leverages estimations of a customer’s willingness to pay to allow for a dynamic pricing of those services and the incorporation of profitability considerations into the recommendation process. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect individualized preference and willingness-to-pay data. Compared to other state-of-the-art approaches, our system requires significantly less customer input before making a recommendation, does not suffer from the usual sparseness of data and cold-start problems of collaborative filtering systems, and, as is shown in an empirical evaluation with a sample of 428 customers in the machine tool market, does not diminish the predictive accuracy of the recommendations offered.     

On the design of a self-medication web application built on linked open data

Open Self-Medication¹  is a Web application that better informs people when treating undiagnosed medical ailments with unprescribed, over the counter drugs, i.e., self-medicating. The application achieves this goal by providing a set of functionalities that ensure safety and efficiency of this practice. The system’s most critical operations are processed using a self-medication knowledge base, expressed in OWL, which has been inductively built on medical information obtained from a similar French project. A main characteristic of this application is that almost all the data processed by the system and presented to the end-user comes from a subset of the LOD data sets, namely DrugBank, DailyMed, Sider and DBPedia. This paper motivates the design of such an application, provides the design choices, describes some implementation details and presents lessons learned and future work.     

Enhancing social communication of children with high-functioning autism through a co-located interface

This study evaluated the effectiveness of a 3-week intervention in which a co-located cooperation enforcing interface, called StoryTable, was used to facilitate collaboration and positive social interaction for six children, aged 8–10 years, with autistic spectrum disorder (ASD). The intervention focused on exposing pairs of children to an enforced collaboration paradigm while they narrated a story. Pre- and post-intervention tasks included a “low technology” version of the storytelling device and a non storytelling play situation using a free construction game. The outcome measure was a structured observation scale of social interaction. Results demonstrated progress in three areas of social behaviors. First, the participants were more likely to initiate positive social interaction with peers after the intervention. Second, the level of shared play of the children increased from the pre-test to the post-test and they all increased the level of collaboration following the intervention. Third, the children with ASD demonstrated lower frequencies of autistic behaviors while using the StoryTable in comparison to the free construction game activity. The implications of these findings are discussed in terms of the effectiveness of this intervention for higher functioning children with ASD.     

Enhancing mobile Web access using intelligent recommendations

Intelligent Web recommendations derived from frequent user Web-access patterns can help typical mobile users efficiently navigate standard Web sites. To facilitate mobile Web access, mobile clients' display resolution must gradually increase. Additionally, servers need a simple and elegant solution for displaying content effectively on mobile devices. We propose an implicit server-side approach using intelligent Web recommendations that can significantly enhance the mobile-browsing experience.     

基于位置和开放链接数据的旅游推荐系统综述

利用开放链接数据解决基于位置的推荐系统中存在的信息过载问题是目前的研究热点，并在旅游领域展现出了巨大的潜力。首先给出推荐系统的基本概况；然后对旅游开放链接数据进行了概况；从文献分类、应用分类和研究成果对基于位置和开放链接数据的旅游推荐系统从2014—2018年的相关文献进行了详细考察，并从基于位置的单点推荐、旅游路线推荐、GPS轨迹推荐、基于媒介的地理标签推荐、基于本体的位置推荐和基于位置的朋友推荐等6类典型的应用进行分类考察，最后对全文并为该领域指明了研究方向。     

Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system

In the last few years, social media systems have experienced a fast growth. The amount of content shared in these systems increases fast, leading users to face the well known “interaction overload” problem, i.e., they are overwhelmed by content, so it becomes difficult to come across interesting items. To overcome this problem, social recommender systems have been recently designed and developed in order to filter content and recommend to users only interesting items. This type of filtering is usually affected by the “over-specialization” problem, which is related to recommendations that are too similar to the items already considered by the users. This paper proposes a friend recommender system that operates in the social bookmarking application domain and is based on behavioral data mining, i.e., on the exploitation of the users activity in a social bookmarking system. Experimental results show how this type of mining is able to produce accurate friend recommendations, allowing users to get to know bookmarked resources that are both novel and serendipitous. Using this approach, the impact of the “interaction overload” and the “over-specialization” problems is strongly reduced.     

Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce

Although research on flow experience has recently received much attention, few studies have been published on the perceived interpersonal interaction factors of consumers and their influence in social commerce. In addition, few studies have focused on the impact of interpersonal interaction factors on flow experience. Drawing on the stimulus-organism-response framework, this study examines the impact of interpersonal interaction factors (perceived expertise, similarity, and familiarity) on the formation of flow experience and its subsequent effects on purchase intention in the context of social commerce. We investigate whether the impact of the three interpersonal interaction factors on flow experience differs between young and old users. We conduct a survey and collect 349 responses from users of a social shopping site in China. Our results indicate that interpersonal interaction factors positively relate to flow experience and subsequently influence purchase intention. We also find differences between young and old users in this area.