From April 7th to 12th, 2025, two researchers from UCDS visited Kuching, Sarawak. This visit, made possible through our long-standing collaboration with UNIMAS, was a vibrant mix of education, cultural exchange, and engaging discussions on the future of AI in society. dr. Cheah Wai Shiang, Associate Professor at UNIMAS was again our main point of contact for engaging with various communities. The trip included talks, discussions and education (at primary, secondary, vocational and university levels). Read more at Victor’s website.
de Boer, V., Shoilee, S.B.A. (2025). Linked Open Data for Cultural Heritage. In: Saloul, I., Baillie, B. (eds) The Palgrave Encyclopedia of Cultural Heritage and Conflict. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-61493-5_274-1
As VU UCDS participants in the HEDGE-IoT project, we wrote a blog post detailing the OfficeGraph knowledge graph. You can ready it on the project website:
The HorizonEurope project HEDGE-IoT started January 2024. The 3.5 year project will build on existing technology to develop a Holistic Approach towards Empowerment of the DiGitalization of the Energy Ecosystem through adoption of IoT solutions. For VU, this project allows us to continue with the research and development initiated in the InterConnect project on data interoperability and explainable machine learning for smart buildings.
Researchers from the User-Centric Data Science group will participate in the project mostly in the context of the Dutch pilot, which will run in Arnhems Buiten, the former testing location of KEMA in the east of the Netherlands. In the pilot, we will collaborate closely with the other Dutch partners: TNO and Arnhems Buiten. At this site, an innovative business park is being realized that has its own power grid architecture, allowing for exchange of data and energy, opening the possibility for various AI-driven services for end-users. HEDGE-IoT project kickoff
VU will research a) how such data can be made interoperable and enriched with external information and knowledge and b) how such data can be made accessible to services and end-users through data dashboards that include explainable AI.
The image above shows the Arnhems Buiten buildings and the energy grid (source: Arnhems Buiten)
The award for the Best Network Institute Academy Assistant project for this year goes to the project titled “Between Art, Data, and Meaning – How can Virtual Reality expand visitors’ perspectives on cultural objects with colonial background?” This project was carried out by VU students Isabel Franke and Stefania Conte, supervised by Thilo Hartmann and UCDS researchers Claudia Libbi and Victor de Boer. A project report and research paper is forthcoming but you can see the poster below.
It has pleased NWO to award the HAICu consortium funding under the National Research Agenda programme. In the HAICu project, AI researchers, Digital Humanities researchers, heritage professionals and engaged citizens work together on scientific breakthroughs to open, link and analyze large-scale multimodal digital heritage collections in context.
At VU, researchers from the User-Centric Data Science group will research how to create compelling narratives as a way to present multiple perspectives in multimodal data and how to provide transparency regarding the origin of data and the ways in which it was created. These questions will be addressed in collaboration with the Museum for World Cultures on how citizen-contributed descriptions can be combined with AI-generated labels into polyvocal narratives around objects related to the Dutch colonial past in Indonesia.
The InterConnect project gathers 50 European entities to develop and demonstrate advanced solutions for connecting and converging digital homes and buildings with the electricity sector. Machine Learning (ML) algorithms play a significant role in the InterConnect project. Most prominent are the services that do some kind of forecasting like predicting energy consumption for (Smart) devices and households in general. The SAREF ontology allows us to standardize input formats for common ML approaches and that explainability can be increased by selecting algorithms that inherently have these features (e.g. Decision Trees) and by using interactive web environments like Jupyter Notebooks a convenient solution for users is created where step by step the algorithmic procedures can be followed and visualized and forms an implementation example for explainable AI.
The Semantic Web community has produced a large body of literature that is becoming increasingly difficult to manage, browse, and use. Recent work on attention-based, sequence-to-sequence Transformer neural architecture has produced language models that generate surprisingly convincing synthetic conditional text samples. In this demonstration, we re-train the GPT-2 architecture using the complete corpus of proceedings of the International Semantic Web Conference since 2002 until 2019. We use user-provided sentences to conditionally sample paper snippets, therefore illustrating cases where this model can help at addressing challenges in scientific paper writing, such as navigating extensive literature, explaining the Semantic Web core concepts, providing definitions, and even inspiring new research ideas.
