57 research outputs found
Knowledge Graph Based Hard Drive Failure Prediction
No full textThe hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art
Automated GDPR Contract Compliance Verification Using Knowledge Graphs
No full textIn the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) data sharing, when consent might not be enough, many small and medium enterprises (SMEs) still depend on contracts—a GDPR basis that is often overlooked due to its complexity. The contract’s lifecycle comprises many stages (e.g., drafting, negotiation, and signing) that must be executed in compliance with GDPR. Despite the active research efforts on digital contracts, contract-based GDPR compliance and challenges such as contract interoperability have not been sufficiently elaborated on yet. Since knowledge graphs and ontologies provide interoperability and support knowledge discovery, we propose and develop a knowledge graph-based tool for GDPR contract compliance verification (CCV). It binds GDPR’s legal basis to data sharing contracts. In addition, we conducted a performance evaluation in terms of execution time and test cases to validate CCV’s correctness in determining the overhead and applicability of the proposed tool in smart city and insurance application scenarios. The evaluation results and the correctness of the CCV tool demonstrate the tool’s practicability for deployment in the real world with minimum overhead
Consent through the lens of semantics: state of the art survey and best practices
No full textThe acceptance of the GDPR legislation in 2018 started a new technological shift towards achieving transparency. GDPR put focus on the concept of informed consent applicable for data processing, which led to an increase of the responsibilities regarding data sharing for both end users and companies. This paper presents a literature survey of existing solutions that use semantic technology for implementing consent. The main focus is on ontologies, how they are used for consent representation and for consent management in combination with other technologies such as blockchain. We also focus on visualisation solutions aimed at improving individuals’ consent comprehension. Finally, based on the overviewed state of the art we propose best practices for consent implementation
Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed Consent
No full textThe enforcement of the GDPR in May 2018 has led to a paradigm shift in data protection. Organizations face significant challenges, such as demonstrating compliance (or auditability) and automated compliance verification due to the complex and dynamic nature of consent, as well as the scale at which compliance verification must be performed. Furthermore, the GDPR’s promotion of data protection by design and industrial interoperability requirements has created new technical challenges, as they require significant changes in the design and implementation of systems that handle personal data. We present a scalable data protection by design tool for automated compliance verification and auditability based on informed consent that is modeled with a knowledge graph. Automated compliance verification is made possible by implementing a regulation-to-code process that translates GDPR regulations into well-defined technical and organizational measures and, ultimately, software code. We demonstrate the effectiveness of the tool in the insurance and smart cities domains. We highlight ways in which our tool can be adapted to other domains
The smashHitCore ontology for GDPR-compliant sensor data sharing in smart cities
No full textThe adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case
Implementing Informed Consent with Knowledge Graphs
No full textThe GDPR legislation has brought to light one’s rights and has highlighted the importance of consent, which has caused a major shift in how data processing and sharing are handled. Data sharing has been a popular research topic for many years, however, a unified solution for the transparent implementation of consent, in compliance with GDPR that could be used as a standard, has not been presented yet. This research proposes a solution for implementing informed consent for sensor data sharing in compliance with GDPR with semantic technology, namely knowledge graphs. The main objectives are to model the life cycle of informed consent (i.e. the request, comprehension, decision and use of consent) with knowledge graphs so that it is easily interpretable by machines, and to graphically visualise it to individuals in order to raise legal awareness of what it means to consent and the implications that follow.</p
Classifying Scientific Topic Relationships with SciBERT
No full textCurrent AI systems, including smart search engines and recommendation systems tools for streamlining literature reviews, and interactive question-answering platforms, are becoming indispensable for researchers to navigate and understand the vast landscape of scientific knowledge.Taxonomies and ontologies of research topics are key to this process, but manually creating them is costly and often leads to outdated results.This poster paper shows the use of SciBERT model to automatically generate research topic ontologies.Our model excels at identifying semantic relationships between research topics, outperforming traditional methods.This approach promises to streamline the creation of accurate and up-to-date ontologies, enhancing the effectiveness of AI tools for researchers
Making Sense of Consent with Knowledge Graphs
No full textThe General Data Protection Regulation (GDPR) [1], which came into effect in May 2018, triggered a major technological shift towards greater transparency in data sharing [2]. An emphasis has been put on the rights of individuals, especially European citizens, regarding their personal data sharing. Despite the fact that data sharing has been a widely researched topic for years, there is a lack of solutions that enable the transparent implementation of consent in an easily understandable manner for both humans and machines in compliance with GDPR. This thesis presents a knowledge graphbased approach for consent representation and implementation that supports machines and humans in making sense of consent through its entire life-cycle. This is achieved by combining approaches from the computer science and behavioral change fields and by considering the comprehension needs of both humans and machines. This thesis demonstrates the feasibility of the approach through its successful adoption and implementation in two industrial data sharing use cases in the smart cities and insurance domains from the smashHit and CampaNeo projects. The main objectives of this work are to use knowledge graphs to semantically represent the life-cycle of informed consent and to visually represent it to individuals in effort to increase legal awareness of the significance of consent [3]. The visualisations place emphasis on the pre- and post-consent stages, including how to request consent in an informed manner and what happens to one's data after consent is given. Incentives, in the form of gamification, are further used to overcome issues such as blindly given consent and to raise the consent rates
Making sense of consent with knowledge graphs
No full textN/AThe General Data Protection Regulation (GDPR) [1], which came into effect in May 2018, triggered a major technological shift towards greater transparency in data sharing [2]. GDPR has put emphasis on individuals’ rights and the importance of informed consent for the personal data sharing of European citizens. Despite the fact that data sharing has been a widely researched topic for years, there is a lack of solutions that enable the transparent implementation of consent in an easily understandable manner for both humans and machines in compliance with GDPR. This thesis presents a knowledge graph-based approach for consent management that supports machines and humans in making sense of consent through its entire life- cycle. This is achieved by combining approaches from the computer science and behavioural change fields and by considering the comprehension needs of both humans and machines.
This thesis demonstrates the feasibility of the approach through its successful adoption and implementation in two industrial data sharing use cases in the smart cities and insurance domains from the smashHit and CampaNeo projects. The main objectives of this work are to use knowledge graphs to model the life-cycle of informed consent (i.e. the request, comprehension, decision, and use of consent) and to graphically visualise it to individuals in effort to increase legal awareness of the significance of consent and the implications that follow [3]. The visualisations place emphasis on the pre-and post-consent stages, including how to request consent in an informed manner and what happens to one’s data after consent is given. Incentives, in the form of gamification, are further used to overcome issues such as blindly given consent and to raise the consent rates.Anelia Malcheva KurtevaDissertation University of Innsbruck 202
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