1,721,023 research outputs found
Interactively using Semantic Web knowledge: Creating scalable abstractions with FacetOntology
No full textThe amount of knowledge accessible on the Semantic Web is growing, and there is a need for a scalable solution to facilitate exploring that data. Currently approaches to exploring Semantic Web data either focus on exploring resources individually, following links during exploration, and making little use of collated data, or take the approach of collating and aligning multiple sources into one store for one purpose, and hand-crafting a specific browsing interface onto it. We present an approach that provides a scalable browsing interface, which can browse knowledge from the Semantic Web at will. Our approach creates abstractions of knowledge, collated into facets, which are described using FacetOntology. FacetOntology facilitates describing facets from RDF data, suitable for use in creating datasets for faceted browsing
Bubbling menus: a selective mechanism for accessing hierarchical drop-down menus
No full textThis paper introduces bubbling menus, a new design for cascading drop-down menus. Bubbling menus combine the bubble cursor [10] with directional mouse-gesture techniques to facilitate the access of certain items in a menu, such as frequently selected items. Through an extensive iterative design process, we explore bubbling menus in the context of adaptive and customizable user interfaces. Unlike other adaptation and customization techniques such as split menus, bubbling menus do not disrupt the original structure of menus and enable the activation of menus far from a menu bar. Results from two evaluation studies presented in the paper show that bubbling menus provide an effective alternative to accelerate menu selections tasks
A longitudinal study of exploratory and keyword search
No full textDigital libraries are concerned with improving the access to collections to make their service more effective and valuable to users. In this paper, we present the results of a four-week longitudinal study investigating the use of both exploratory and keyword forms of search within an online video archive, where both forms of search were available concurrently in a single user interface. While we expected early use to be more exploratory and subsequent use to be directed, over the whole period there was a balance of exploratory and keyword searches and they were often used together. Further, to support the notion that facets support exploration, there were more than five times as many facet clicks than more complex forms of keyword search (boolean and advanced). From these results, we can conclude that there is real value in investing in exploratory search support, which was shown to be both popular and useful for extended use of the system.</p
Effective Benchmarking for RDF Stores Using Synthetic Data
No full textRDF stores are showing consistent performance improvements, with benchmarks showing that several are capable of effectively storing and querying over 109 triples. However, detailed information regarding the capabilities of the available systems is limited due to the fact that current benchmarks provide little configurability, and little depth on the strengths and weaknesses of the stores they test. This paper considers the deficiencies of current benchmarks with regards to measuring the performance of RDF stores, and goes on to describe the creation of a new system to run a greater variety of tests using highly configurable synthetically generated datasets. Finally, the benchmark is applied to existing large scale stores, and the results interpreted. This work is intended to inform future RDF store development, and allow application developers to choose a system appropriate to their specific needs
“Honey=sugar” means unhealthy: investigating how people apply knowledge to rate food’s healthiness
No full textWhile previous research studied the high level attributes people consider when they assess the healthiness of food they are familiar with, little work has looked at how people assess arbitrary, potentially unfamiliar, food to decide whether it is a healthy choice. Since there is a growing body of work in Ubicomp around health practices, including systems to support healthy eating, it is important to understand how people apply the knowledge they have to food decisions. In our studies we identified 8 attributes participants use for determining if they think a food is “healthy” or not. Based upon our analysis, we reflect on current system designs and propose four future design opportunities: capturing context of healthy eating, preparation and reflection on healthy eating understanding, sharing understanding and in situ information support
Richtags: Cross Repository Browsing
No full textrichtags allows you to search across multiple repositories from numerous institutions covering hundreds of disciplines for research that is of interest to you
Wellth Creation: Using Computer Science to Support Proactive Health
Full text linkBy moving beyond logical data collection and engaging people on a subconscious and emotional level, computing technology could change cultural norms and thereby more effectively motivate lifestyle changes that prevent disease
Social Interaction around Diet Applications: An Initial Study
No full textAs being threatened by overweight and obesity, more and more people start thinking how to change the way they eat. However, less evidence shows current diet-related applications really work and current design mainly focuses on nutrition value and are not tailored to specific person. In this paper, we try to learn from social science to investigate two popular diet products' forums to see what social interaction happens and what elements related to those social interaction. Then we find out contextual information and emotion are related to social conversation on forum which help people find similar buddies to solve problem and validate opinions and understandings. We argue we should take into account those information and social interaction in our future design to better support diet
Scared or Naive? An Exploratory Study on Users Perceptions of Online Privacy Disclosures
No full textAs a result of various industry regulations service providers such as websites and app developers are required to explain the ways in which they process the personal data of service users. These “privacy disclosures”, which aim to inform users and empower them to control their privacy, take several forms. Among these forms are the privacy policy, the cookie notice and, on smart phones, the app permission request. The interaction problems with these different types of disclosure are relatively well understood – habituation, inattention and cognitive biases undermine the extent to which user consent is truly informed. User understanding of the actual content of these disclosures, and their feelings toward it, are less well understood, though. In this paper we report on a mixed-methods study that explored these three types of privacy disclosure and compare their relative merits as a starting point for the development more meaningful consent interactions. We identify four key findings – heterogeneity of user perceptions and attitudes to privacy disclosures, limited ability of users to infer data processing outputs and risks based on technical explanations of particular practices, suggestions of a naïve model of “cost justification” rather cost-benefit analysis by users, and the possibility that consent interactions are valuable in themselves as a means to improve user perceptions of a service
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