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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Sponsorships

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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.

DiSHACLed

The DiSHACLed – dissecting data workflows using SHACL – project aims to raise the efficiency of Flemish DSSPs within the European data ecosystem, by starting from the three partners in this consortium: Sirus, Redpencil and Inuits. The general objective of the DiSHACLed project is to increase the efficiency of our data intermediaries within the European data ecosystem. Today each intermediary provides their own tooling within a certain domain. In this project we focus on three categories of hindrances. A first hindrance to data flowing flawlessly is the fact that we are today not able to (i) discover datasets and data services automatically: they need to be manually selected and the URLs to the datasets and their services are hard-coded within applications. This further hinders evolvability as well: as each application is hard-coded against a specific service, it has no mechanism to later switch to another service. Another hindrance is the fact that (ii) tooling from intermediaries – even when this tooling is made available as open-source software – cannot technically be plugged in the tooling of other vendors. It requires manual integration of both technical protocols as the semantics of the domain models. Finally, getting (iii) an overview of the available data, and generating forms for missing data, is at this moment a manual task.

DS4Skills-GO

The DS4Skills-GO project aims to deploy a European Skills Data Space through eight large and sustainable use cases. Building on the infrastructure developed under the EDGE-Skills project, DS4Skills-GO demonstrates the practical benefits of the skills data space, significantly impacting broader EU policies by supporting the European Data Strategy and enhancing digital skills and employment opportunities across Europe. DS4Skills-GO is structured around six key work packages, each focusing on a critical aspect of the project. The project ensures efficient management and quality assurance, establishes robust governance frameworks, maintains data space operations, and focuses on capacity-building, knowledge-sharing, and policy recommendations. Communication, stakeholder engagement, sustainability, and synergy with existing initiatives are also integral parts of the project. The project adopts a "use cases first" approach, leveraging previous work from DS4Skills and EDGE-Skills. It will deploy the skills data space in eight large use cases, addressing challenges like lifelong learning, employee retention, research on learning paths, upskilling, and reskilling. Use case orchestrators will purchase data space services to launch and operate their initiatives. By showcasing the value of the skills data space, DS4Skills-GO aims to enhance digital skills and support the digital transformation of the European economy. The project aligns with the European Data Strategy, emphasising the importance of a thriving data ecosystem while preserving privacy, security, safety, and ethical standards. DS4Skills-GO's efforts are expected to foster innovation, improve education and training policies, and support employment strategies across Europe. DS4Skills-GO addresses immediate skill development needs and contributes to the EU's long-term strategic goals, promoting a skilled and adaptable workforce for the digital age.

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.

LDT4SSC

The EU project Local Digital Twins for Smart and Sustainable Communities (LDT4SSC) aims to create a robust, scalable, interconnected ecosystem of Local Digital Twins (LDTs), and to galvanise the market for new LDT-based services. This will build on prior achievements, such as the LDT Toolbox and the European data space for smart communities (DS4SSCC-DEP). These efforts will form a resilient LDT infrastructure, enabling European communities to optimise urban services, share resources effectively, and address common challenges like climate change, air quality, waste management, and energy efficiency. By enhancing these infrastructures, communities will be better positioned to use digital twin technology across a range of domains, including urban development, mobility, climate action, and resource management.

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.

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.

SecuWeb

SecuWeb aims to strengthen data security in companies and organisations in Flanders, Wallonia and France. The project investigates how innovative technologies such as Solid, Blockchain, Quantum Internet, 5G, AI and IoT can contribute to safer data usage. Using demonstrators and use cases, we investigate how we can implement these technologies in companies and organisations. We focus on the sectors of e-health, industry 4.0, mobility and food, which are important spearheads for the economy of our regions.

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.

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.

SolidLab

To reinforce the trust of its citizens and help companies compete against internet giants, Flanders is leading the race to set a new standard in data protection. A EUR 7 million government investment is providing a major boost for the practical implementations of a technology called Solid. Three of Flanders’ leading universities are joining forces in SolidLab Vlaanderen.

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.

W4S

Shipbuilding and maritime supply chain organisations are traditionally based in coastal regions in NWE. Over the last decades coastal regions and the shipping industry alike faced demographic and innovation capacity challenges as especially younger people left these regions and ship(supply) production was largely outsourced to low income countries. As climate change is there to stay, maritime supply chain organisations in coastal regions are challenged to increase their innovation capacity to become fit again to contribute to mitigation solutions. Our W4S approach targets upscaling production and sizes of wing- and rotorsail; in 4 pilot actions we will test and showcase production-, test wingsails and rotorsails on ships and build the body of knowledge for further uptake of Wind Assisted Ship Propulsion (WASP) in the NWE. This will be realized by our excellent partnership that includes, manufacturers, developers of rotor- and wing sails, smart specialization strategists, university and higher education partners and last but not least our launching customers, our shipowners. On the one hand, we will lead WP3, Action 2 to optimize the routes of WAPS vessels based on up-to-date weather data: this allows to optimize the wind propulsion and thus improves the performance of wind-assisted vessels. On the other hand, we will participate in the regional innovations and strategy plan in both WP1 and WP3, focusing on value chain innovation analysis and economics of upscaling for the production of wind-propulsion techniques in the NWEs area. We will also contribute to deliverable outputs, such as data collection and analysis, and scientific publications.

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