KNowledge On Web Scale

The Knowledge on Web scale (KNoWS) group of IDLab - Ghent University is an experienced team of creative researchers in advanced data technologies. Our mission involves enabling optimal flows of public and protected data between different parties, and our ultimate goal is for data to impact people’s lives positively in ways they choose themselves. Learn more about KNoWS.

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Vacancies

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Project

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PhD

Are you passionate about the same topics as we are and want to do your PhD with us? Find out more about the possibilities and how to apply.

Research stay

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Master thesis

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Sponsorships

Are you looking for a sponsorship for a course or conference? Do you identify as a women or Black person? Find out more about our sponsorships and how to apply.

BOCEMON

BOCEMON is an imec.ICON project, consisting of three industry partners (Quares, DYAMAND and Daikin) and various IDLab teams (PreDICT, KNoWS, HomeLab, Antwerp) within the building management domain. It's a 2-year project that will start 1/10/2024. Project aim: To convert office buildings into truly sustainable, comfortable, healthy and flexible spaces extra services are needed to help building and facility managers to decide on building upgrade investments, optimally allocate office spaces and dynamically control HVAC systems. Such services require access to a broad range of data sources (HVAC data, user feedback, BIM models, etc.) via easy accessible interfaces, but current deployment requires a lot of (manual) work as data is often distributed over many systems, using different protocols and data models. There is a clear need to extend the interoperability capabilities between stakeholders and service providers involved in the building lifecycle, to enable a rapid scaling to many buildings. BOCEMON will develop tools to allow service providers to prototype, test and deploy their services much faster. By using a semantic data model a wide range of building services will be supported and data exchange with diverse stakeholders will be improved. Accompanying tools will be designed by IDLab.KNoWS for easy service design, to explore the available data in the semantic data model, and by IDLab.PreDICT to automatically visualize sensor data on floor plans. IDLab.KNoWS will collaborate with DYAMAND to design an automatic onboarding service to improve the discovery and mapping of sensors and actuators onto this domain model. Hybrid AI based transfer learning methodologies will be designed by IDLab. PreDICT and will allow the efficient transfer of ML models (e.g. to derive trends & patterns on user behavior, occupancy, building health state, etc.) between different building contexts. AI based algorithms will be designed to support investment decisions to realize sustainable and comfortable buildings, and realize a fine-grained occupant-centric and interoperable HVAC control.

ERA-KG

Bilateral collaboration with the European Agency for Railways (ERA) to create and evolve a semantic interoperability layer that integrates the different base registries maintained by the agency.

FRACTION

Today, centralized stores govern the storage and processing of Big Data. Due to regulation (e.g. GDPR) and increasing awareness of people about data sensitivity, a paradigm shift towards decentralization is imminent. This allows people to control their own personal data, by guarding all public and private data they or others create about them in a vault, and selectively granting access to people and organizations of their choice. Future uses of Big Data are thus bound to shift from a small number of large datasets to a large number of small datasets. As such, the fundamental assumptions on which the current approaches are built to deal with the characteristics of Big Data (Volume, Variety & Velocity) are no longer valid, i.e. volume cannot be tackled by centralising data in a single location, high velocity data streams cannot be sent to a centralised data center and the variety problem cannot be resolved by imposing a single data format. FRACTION supports the shift to a decentralized approach by leveraging Semantic Web technologies. It will investigate 1. algorithms that autonomously distribute the analytics across the decentralized network, while hiding its complexity to the user, 2. decentralized and user-friendly data access control policies, and 3. methods to exploit the heterogeneity of the decentralized network to improve scalability and performance of the analytics.

MareGraph

MareGraph will build a data architecture for cost-efficient, flexible and sustainable data sharing for a Marine Knowledge Graph. In this project we collaborate with the Flemish Marine Institute (VLIZ), Digital Flanders and the Italian National Research Council (CNR).

Onto-DESIDE

The Onto-DESIDE (Ontology-based Decentralized Sharing of Industry Data in the European Circular Economy) will address the main challenges facing industry that prevent the efficient sharing of data and making it understandable and usable by humans and machines. The project targets four core project outcomes: - A shared vocabulary in the form of network of ontologies. - An open circularity platform, i.e. a secure and privacy-preserving decentralised data sharing platform using RML, Solid, Comunica, Verifiable Credential, and blockchain technologies. - Methods to find, analyse and assess the new circular value chain configurations. - Validation– demonstrating and quantifying the potential for increased retainment of value.

OSLO

The Flemish government wants to optimize its services and make the exchange of information smoother. But government services to citizens and entrepreneurs are supported by specialized applications from various software suppliers. That is why there is a need for an unambiguous standard: OSLO (Open Standards for Linking Organizations).

PACSOI

PACSOI is a project focused on enabling Solid for healthcare. It’s a two-year project started 1/06/2024. Project aim: Healthcare ecosystems are siloed which makes it difficult to integrate personal health data with clinical data, access and jointly analyze it to generate meaningful insights. These silos are even more challenging for the increasingly common remote monitoring & digital therapeutics tools that gather real-world data & insights. This creates a barrier to leverage these data to achieve a holistic & longitudinal view on the patient’s condition. This is especially troubling for patients with chronic or complex conditions who receive care from multiple providers across settings. Currently, the ownership, control, transparency & consent of health data use & insight generation are outlined in data processing agreements between patients, care providers & third-party companies. This is a complex & time-consuming process, lacking patient involvement in data control decisions, which raises ethical concerns.

Serendipity Engine

We increasingly rely on algorithmically generated recommendations to navigate in both online and offline contexts: listening to music on streaming platforms, reading news online, or following recommendations about activities and events in your favorite city. These recommender systems help us dealing with the abundance of available information, but at the same time raise questions about their impact on individual citizens and society. The Serendipity Engine project sets out to address these challenges and support societal stakeholders in designing recommender systems to foster serendipity in public contexts.

SHARCS

Data pods, for example based on the emerging Solid technology, securely store personal data and allow n-to-m data sharing between individuals and apps/third parties. However, there are still quite some research challenges remaining. These relate to which data are shared and with whom they are shared, but also to the validity of the data that are shared. The SHARCS project aims to solve a number of these challenges for the case of secure and selective sharing of accredited personal data.

Solid4Media

With the active involvement of a representative group of Flemish media users, this living lab investigates the potential of Solid technology in a real media context. The goal is to migrate users' media profiles to a pod infrastructure and provide existing and new media services from there. The project proposal focuses on three important use cases identified by Flemish media stakeholders: (i) personal recommendations, (ii) trust through transparency and consent control, and (iii) targeted advertisements. By practically realizing the Solid infrastructure, the technical feasibility and scalability of a future-oriented decentralized media infrastructure is investigated and the concrete added value this can offer for existing and new media services is examined, both from a business and end-user perspective.

Sytadel

Sytadel will provide technical data spaces architecture and design principles for actors who want to participate in the logistics data spaces in the future, together with open-source components and/or open software implementation guidelines.

TRIPLE

The TRIPLE project, a collaborative effort between the SIB Swiss Institute of Bioinformatics, the University of Ghent and the IOCB Prague, aims to boost the (re)usability of existing knowledge graph resources and improve software tools for RDF data access, documentation and data model visualization. In addition, TRIPLE will increase interoperability between existing public SPARQL endpoints and private data stored in Solid Pods, thus creating an ecosystem of research data that can be seamlessly integrated through efficient and expressive federated SPARQL queries.

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