Suvodeep Mazumdar

Exploring user and system requirements of linked data visualization through a visual dashboard approach

One of the open problems in Semantic Web research is which tools should be provided to users to explore linked data. This is even more urgent now that massive amount of linked data is being released by governments worldwide. The development of single dedicated visualization applications is increasing, but the problem of exploring unknown linked data to gain a good understanding of what is contained is still open. An effective generic solution must take into account the users point of view, their tasks and interaction, as well as the systems capabilities and the technical constraints the technology imposes. This paper is a first step in understanding the implications of both, user and system by evaluating our dashboard-based approach. Though we observe a high user acceptance of the dashboard approach, our paper also highlights technical challenges arising out of complexities involving current infrastructure that need to be addressed while visualizing linked data. In light of the findings, guidelines for the development of linked data visualization and manipulation are provided.

A knowledge dashboard for manufacturing industries

The manufacturing industry offers a huge range of opportunities and challenges for exploiting semantic web technologies. Collating heterogeneous data into semantic knowledge repositories can provide immense benefits to companies, however the power of such knowledge can only be realised if end users are provided visual means to explore and analyse their datasets in a flexible and efficient way. This paper presents a high level approach to unify, structure and visualise document collections using semantic web and information extraction technologies.

Affective graphs: The visual appeal of Linked Data

The essence and value of Linked Data lies in the ability of humans and machines to query, access and reason upon highly structured and formalised data. Ontology structures provide an unambiguous description of the structure and content of data. While a multitude of software applications and visualization systems have been developed over the past years for Linked Data, there is still a significant gap that exists between applications that consume Linked Data and interfaces that have been designed with significant focus on aesthetics. Though the importance of aesthetics in affecting the usability, effectiveness and acceptability of user interfaces have long been recognised, little or no explicit attention has been paid to the aesthetics of Linked Data applications. In this paper, we introduce a formalised approach to developing aesthetically pleasing semantic web interfaces by following aesthetic principles and guidelines identified from literature. We apply such principles to design and develop a generic approach of using visualizations to support exploration of Linked Data, in an interface that is pleasing to users. This provides users with means to browse ontology structures, enriched with statistics of the underlying data, facilitating exploratory activities and enabling visual query for highly precise information needs. We evaluated our approach in three ways: an initial objective evaluation comparing our approach with other well-known interfaces for the semantic web and two user evaluations with semantic web researchers.

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the users ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive.

Exploiting Linked Open Data to Uncover Entity Types

Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes.

NL-Graphs: A Hybrid Approach toward Interactively Querying Semantic Data

A variety of query approaches have been proposed by the semantic web community to explore and query semantic data. Each was developed for a specific task and employed its own interaction mechanism; each query mechanism has its own set of advantages and drawbacks. Most semantic web search systems employ only one approach, thus being unable to exploit the benefits of alternative approaches. Motivated by a usability and interactivity perspective, we propose to combine two query approaches (graph-based and natural language) as a hybrid query approach. In this paper, we present NL-Graphs which aims to exploit the strengths of both approaches, while ameliorating their weaknesses. NL-Graphs was conceptualised and developed from observations, and lessons learned, in several evaluations with expert and casual users. The results of evaluating our approach with expert and casual users on a large semantic dataset are very encouraging; both types of users were highly satisfied and could effortlessly use the hybrid approach to formulate and answer queries. Indeed, success rates showed they were able to successfully answer all the evaluation questions.

Crowdsourcing to enhance insights from satellite observations

Insights from satellite observations are increasingly being used to enhance a range of domains from highly specialised scientific research through to everyday applications directly benefiting members of the public. A particular category of satellite observations—Earth Observations (EO)—is concerned with capturing information regarding the Earth’s atmospheric and environmental conditions and observing human activity and its impact on the Earth’s surface. A growing number of technologies and services heavily rely on EO data and the rapidly improving fidelity, coverage, timeliness and accessibility of such observations are providing significant opportunities for new applications of economic and societal benefit. With the increasing importance, relevance and size of EO data sets, it is critical to understand how the value of such data can be maximised by complementing EO with other sources of data and efficiently making complex interpretations and decisions. The wide adoption and availability of smartphones, Internet devices and increased accessibility to information has paved the way for large numbers of citizens and communities to participate in scientific, technological, societal and decision-making activities. This chapter discusses the experience of the European Space Agency funded Crowd4Sat project led by the University of Sheffield that investigated different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites products and services.

Citizen science for observing and understanding the Earth

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges.

Big Data and Cyber Security: A Visual Analytics Perspective

With organisations and governments significantly investing in cyber defenses, there is an urgent need to develop tools and technologies to help security professionals understand cyber security within their application domains. A critical aspect of this is to develop and maintain situation awareness of security aspects within cyber infrastructures. Visual analytics provide support to security professionals to help understand evolving situations and the overall status of systems, particularly when dealing with large volumes of data. This chapter explores situation awareness in cyber security in more detail, aligning design recommendations for visual analytics to assist security professionals with progressive levels of situation awareness.

Lessons learned using wi-fi and Bluetooth as means to monitor public service usage

Facets of urban public transport such as occupancy, waiting times, route preferences are essential to help deliver improved services as well as better information for passengers to plan their daily travel. The ability to automatically estimate passenger occupancy in near real-time throughout cities will be a step change in the way public service usage is currently estimated and provide significant insights to decision makers. The ever-increasing popularity and abundance of mobile devices with always-on Wi-Fi/Bluetooth interfaces makes Wi-Fi/Bluetooth sensing a promising approach for estimating passenger load. In this paper, we present a Wi-Fi/Bluetooth sensing system to detect mobile devices for estimating passenger counts using public transport. We present our findings on an initial set of experiments on a series of bus/tram journeys encapsulating different scenarios over five days in a UK metropolitan area. Our initial experiments show promising results and we present our plans for future large-scale experiments.