User Interaction with Linked Data: An Exploratory Search Approach

NoIt is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed

Ontology-based Domain Diversity Profiling of User Comments

Diversity has been the subject of study in various disciplines from biology to social science and computing. Respecting and utilising the diversity of the population is increasingly important to broadening knowledge. This paper describes a pipeline for diversity profiling of a pool of text in order to understand its coverage of an underpinning domain. The application is illustrated by using a domain ontology on presentation skills in a case study with 38 postgraduates who made comments while learning pitch presentations with the Active Video Watching system (AVW-Space). The outcome shows different patterns of coverage on the domain by the comments in each of the eight videos

A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments

This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain

Assisting User Browsing over Linked Data: Requirements Elicitation with a User Study

There are growing arguments that linked data technologies can be utilised to enable user-oriented exploratory search systems for the future Internet. Recently, search over linked data has been studied in different domains and contexts. However, there is still limited insight into how conventional semantic browsers over linked data can be extended to empower exploratory search, which is open-ended, multi-faceted and iterative in nature. Empirical user studies in representative domains can identify problems and elicit requirements for innovative functionality to assist user exploration. This paper presents such an approach - a user study with a uni-focal semantic data browser over several datasets linked via domain ontologies is used to inform what intelligent features are needed in order to assist exploratory search through linked data. We report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. A semantic signposting approach for extending a semantic data browser is proposed as a way to address the derived requirements

Exploring exploratory search: a user study with linked semantic data

The maturation of semantic technologies and the growing popularity of the Linked Open Data (LOD) cloud make it possible to expose linked semantic data sets to end users in order to empower a range of analytical tasks taking advantage of knowledge integration and semantic linking. Linked semantic data appears to offer a great potential for exploratory search, which is open-ended, multi-faceted, and iterative in nature. However, there is limited insight into how browsing through linked semantic data sets can support exploratory search. This paper presents a user study with a uni-focal semantic browsing interface for exploratory search through several data sets linked via domain ontologies. The study, which is qualitative and exploratory in nature and uses music as an illustrative domain, examines (i) obstacles and challenges related to user exploratory search in LOD and (ii) the serendipitous learning effect and the role semantics plays in that. The approach and lessons learnt can benefit future human factor studies to evaluate interactive exploration of linked semantic data, as well as technology developers to become aware of issues that have to be addressed in to facilitate exploratory search with LOD

Views in user generated content for enriching learning environments: a semantic sensing approach

Social user-generated content (e.g. comments, blogs) will play a key role in learning environments providing a rich source for capturing diverse viewpoints; and is particularly beneficial in ill-defined domains that encompass diverse interpretations. This paper presents Views - a framework for capturing viewpoints from user-generated textual content following a semantic sensing approach. It performs semantic augmentation using existing ontologies and presents the resultant semantic spaces in a visual way. Views was instantiated for interpersonal communication and validated in a study with comments on job interview videos, achieving over 82% precision. The potential of Views for enriching learning environments is illustrated in an exploratory study by analysing micro-blogging content collected within a learning simulator for interpersonal communication. A group interview with simulator designers evinced benefits for gaining insights into learner reactions and further simulator improvement