Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie

A human factors perspective on volunteered geographic information

This thesis takes a multidisciplinary approach to understanding the unique abilities of Volunteered Geographic Information (VGI) to enhance the utility of online mashups in ways not achievable with Professional Geographic Information (PGI). The key issues currently limiting the use of successful of VGI are the concern for quality, accuracy and value of the information, as well as the polarisation and bias of views within the user community. This thesis reviews different theoretical approaches in Human Factors, Geography, Information Science and Computer Science to help understand the notion of user judgements relative to VGI within an online environment (Chapter 2). Research methods relevant to a human factors investigation are also discussed (Chapter 3). (Chapter 5) The scoping study established the fundamental insights into the terminology and nature of VGI and PGI, a range of users were engaged through a series of qualitative interviews. This led the development of a framework on VGI (Chapter 4), and comparative description of users in relation to one another through a value framework (Chapter 5). Study Two produced qualitative multi-methods investigation into how users perceive VGI and PGI in use (Chapter 6), demonstrating similarities and the unique ability for VGI to provide utility to consumers. Chapter Seven and Study Three brought insight into the specific abilities for VGI to enhance the user judgement of online information within an information relevance context (Chapter 7 and 8). In understanding the outcomes of these studies, this thesis discusses how users perceive VGI as different from PGI in terms of its benefit to consumers from a user centred design perspective (Chapter 9). In particular, the degree to which user concerns are valid, the limitation of VGI in application and its potential strengths in enriching the user experiences of consumers engaged within an information search. In conclusion, specific contributions and avenues for further work are highlighted (Chapter 10)

Framework for IoT Service Oriented Systems

The forth industrial revolution is here, and with it Industry 4.0, which translates in many changes to the industry. With the introduction of paradigms like Internet of Things, Cyber Physical Systems or Cloud Computing, the so called Smart Factories are becoming a main part of today’s manufacturing systems. The vf-OS Project, where this thesis falls, intends to be an Open Operating System for Virtual Factories where the overall network of a collaborative manufacturing and logistics environment can be managed and thus enabling humans, applications and devices to communicate and interoperate in an interconnected operative environment. This thesis intends to contribute to the vision that any kind of sensor or actuator plugged to the virtual factory network, becomes promptly accessible in the operative environment and the services that it provides can be accessed and used by any API composing the system. Finally, it also aims to prove that an IoT Service Oriented Sys-tem constituted of open software components can be of great assistance and provide numerous contributions to the emerging Industry 4.0 and consequently to the Factories of the Future. With that aim, this thesis will focus on the development of two out of five inter-connected applications that answer not only to different use case scenarios presented in the vf-OS but also provide solutions to answer a practical agriculture scenario, which uses mainly IoT devices and other cutting-edge technologies like cloud compu-ting and FIWARE

Anonymizing and Trading Person-specific Data with Trust

In the past decade, data privacy, security, and trustworthiness have gained tremendous attention from research communities, and these are still active areas of research with the proliferation of cloud services and social media applications. The data is growing at a rapid pace. It has become an integral part of almost every industry and business, including commercial and non-profit organizations. It often contains person-specific information and a data custodian who holds it must be responsible for managing its use, disclosure, accuracy and privacy protection. In this thesis, we present three research problems. The first two problems address the concerns of stakeholders on privacy protection, data trustworthiness, and profit distribution in the online market for trading person-specific data. The third problem addresses the health information custodians (HICs) concern on privacy-preserving healthcare network data publishing. Our first research problem is identified in cloud-based data integration service where data providers collaborate with their trading partners in order to deliver quality data mining services. Data-as-a-Service (DaaS) enables data integration to serve the demands of data consumers. Data providers face challenges not only to protect private data over the cloud but also to legally adhere to privacy compliance rules when trading person-specific data. We propose a model that allows the collaboration of multiple data providers for integrating their data and derives the contribution of each data provider by valuating the incorporated cost factors. This model serves as a guide for business decision-making, such as estimating the potential privacy risk and finding the sub-optimal value for publishing mashup data. Experiments on real-life data demonstrate that our approach can identify the sub-optimal value in data mashup for different privacy models, including K-anonymity, LKC-privacy, and ϵ-differential privacy, with various anonymization algorithms and privacy parameters. Second, consumers demand a good quality of data for accurate analysis and effective decision- making while the data providers intend to maximize their profits by competing with peer providers. In addition, the data providers or custodians must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a two-fold solution: (1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semi-trusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup, and (2) we incorporate the Vickrey-Clarke-Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements. Finally, we address the concerns of HICs of exchanging healthcare data to provide better and more timely services while mitigating the risk of exposing patients’ sensitive information to privacy threats. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets