As part of the VU-UTwente collaboration, VU Amsterdam now offers a bachelor program in Creative Technologies. This year, the first batch of students graduated with super nice and creative projects. I was happy to supervise one of these projects, by Filippos Taprantzis. Filippos developed Arcuno, a fully 3D-printable toolkit that can be used to arrange graph-like structures on a table. In particular, we focused on conceptual modeling (e.g. knowledge graphs, ontologies). One of the intentions of ArcUno is to enable a LEGO-building-like experience, which in turn can make the modeling task more intuitive and approachable.
Arcuno was accepted as a demo-paper at the 2026 SEMANTiCS conference in Ghent. In his blog post, Filippos details Arcuno, how it was presented at the conference, and why having 3-D printed components can actually save you when you forget to bring your actual demonstration material to the conference 🙂
From his blog:
“When Victor de Boer, my Graduation Project supervisor, initially proposed the idea of submitting the project to a conference, I could never have predicted that five months later, my friends Stijn, Nicky, Michela, and I would need to pull an all-nighter to remake the entire thing in under seven hours.
Before diving into the story, I would like to briefly describe what ArcUno, the project developed during my Creative Technology thesis, is and what problem it attempts to solve. “
[This post is based on Arjuna Hellingman’s Bachelor AI Thesis]
Religious texts are often rich in metaphor, symbolism, and implicit meaning, making them challenging for modern AI systems. While recent advances in natural language processing have improved concept extraction in domains such as news and scientific literature, much less is known about how these techniques perform on religious texts.
In his Bachelor’s thesis, Arjuna Hellingman investigated whether modern semantic AI models can identify key Buddhist concepts in the Dhammapada, one of the most influential texts in Theravada Buddhism. The study compared a traditional rule-based lexical approach with two embedding-based semantic models, BGE-base and BGE-m3.
To create an evaluation benchmark, three Buddhist monks annotated 120 verses from the Dhammapada using a predefined set of 32 core Buddhist concepts, including suffering, craving, enlightenment, wisdom, and compassion. These annotations were combined into two gold standards: a liberal version that included concepts identified by at least one monk, and a stricter majority version requiring agreement from at least two monks.
The results revealed an interesting trade-off. On the liberal gold standard, the semantic models performed best, identifying a broader range of concepts and achieving higher overall F1 scores. On the stricter majority gold standard, however, the simple rule-based method achieved the highest performance due to its greater precision. Semantic models generally found more relevant concepts but also produced more false positives.
One particularly surprising finding was that the English-focused BGE-base model outperformed the larger multilingual BGE-m3 model on several abstract Buddhist concepts, including craving, wisdom, rebirth, and enlightenment. This suggests that multilingual models do not necessarily have an advantage when working with English translations of religious texts.
Beyond the specific results, the thesis demonstrates the importance of evaluation design in digital humanities research. The choice of gold standard significantly influenced how the models were judged, highlighting that benchmark construction is as important as model selection when applying AI to low-resource cultural and religious domains.
Reference: Arjuna Hellingman. Concept Extraction under Domain Shift: A Comparative Study of Lexical and Semantic Methods in the Dhammapada. Bachelor’s Thesis, Vrije Universiteit Amsterdam, 2026
[This post is based on Gintaré Rutkuté’s Bachelor AI thesis]
As smart meters become increasingly common, large volumes of energy data are being collected in knowledge graphs. However, these datasets often lack important contextual information, such as which building a meter belongs to or where it is physically located. This makes building-level analysis and interpretation difficult.
In her Bachelor’s thesis, Gintarė Rutkutė investigated how a smart meter knowledge graph from the HEDGE-IoT project at Arnhems Buiten could be enriched to support location-aware energy analysis. The original graph contained meter observations, but lacked explicit links between meters, buildings, and locations.
Using semantic web technologies and the SAREF ontology, the thesis added building and meter location information, linked meters to the buildings they measure, and integrated building data from the Kadaster Knowledge Graph. The resulting enriched knowledge graph enabled energy consumption to be analysed at the building level rather than only at the level of individual meters.
The enriched graph was evaluated using eight competency questions developed together with domain experts. These included questions such as which buildings are measured by a meter, which buildings lack meter coverage, and how energy consumption differs between buildings over time. Interactive maps and visualisations showed that the added semantic and spatial context greatly improved the interpretability of the data.
Spatial representation of buildings and their aggregated energy usage.
Power usage figure for two separate buildings on a specific day (details withheld for anonimity)
The thesis demonstrates how knowledge graph enrichment can transform raw IoT measurements into a richer and more useful resource for analysis. By connecting energy data to its physical context, the approach supports more meaningful exploration of building energy use and creates a stronger foundation for future smart energy and energy-sharing applications.
We’re happy to share that the project HUMAIN has been successfully funded under Horizon Europe. HUMAIN focuses on the ethical and human-centric adoption of Artificial Intelligence in mission-driven sectors that are vital to society: Culture, Education and Open Science. The project responds to the rapid uptake of AI—from generative tools in the creative industries to AI-supported teaching and scholarly publishing—and addresses the need for data-informed guidelines and regulatory frameworks that align AI innovation with public values and societal missions.
The User Centric Data Science group at VU will contribute to the project by analysing the use and impact of AI in the cultural heritage and creative sector, helping ensure that AI applications support inclusive, responsible, and human-centered practices
Since some of the landing pages and online triple stores for the Amsterdam Museum Linked Data set were no longer available, I copied the original data to a GitHub repository. https://github.com/biktorrr/AHM
In the past (2011), the data was available int the Europeana Semantic Layer, developed by the EuropeanaConnect project. At this point, the original data is provided as is through this git repository only. If you want to use it, load it into your favourite triple store. This data has been used as a benchmark dataset in multiple Linked Data research projects, including the kgbench repository benchmark http://kgbench.info/ and therefore I feel it makes sense to ensure the original RDF data can be accessed.
If you use this data, please cite it as:
Victor de Boer, Jan Wielemaker, Judith van Gent, Marijke Oosterbroek, Michiel Hildebrand, Antoine Isaac, Jacco van Ossenbruggen, Guus Schreiber (2013). Amsterdam Museum Linked Open Data. Semantic Web – Interoperability, Usability, Applicability, 4(3), 237–243.https://www.semantic-web-journal.net/content/amsterdam-museum-linked-open-data
Last week, I joined the 21st edition of the Extended Semantic Web Conference (ESWC2024) held in Heraklion Crete. The 2004 edition was my first scientific conference ever, and I have been going to many editions ever since, so this feels a bit like my ‘home conference’. General Chair Albert Meroño and his team did a great job and it was overall a very nice conference. Paul Groth wrote a very nice trip report here, but I wanted to collect some thoughts and personal highlights in a short blogpost anyway.
The workshops
The workshops overall were very well organized and the ones I joined were well attended. This has been different in previous editions! The PhD symposium was very lively and I had nice chats with PhD candidates during the symposium lunch.
I then joined the SemDH workshop on Semantic Digital Humanities and its panel discussion in the afternoon, which was really nice. One observation is that many of the talks in SEMMES could have been very interesting for SemDH as well and vice versa. Maybe merging the two would make sense in the future?
The Keynotes
There were three nice keynote speeches, each with its own angle and interesting points.
Elena Simperl gave a somewhat personal history of Knowledge Engineering and the role that machines and humans have in this process. This served as a prelude for the special track on this topic organized by her, Paul Groth and others. Elena called for tools and data for proper benchmarking, introduced the ProVe tool for provenance verification and explored what the roles are of AI (LLM) with respect to Knowledge engineers, domain experts and prompt engineers.
Katariina Kari reflected on 7 Years of Building Enterprise Knowledge Graphs at Zalando and Ikea. This was a very interesting talk about the impact of Knowledge Graphs in industry (she mentioned 7 figure sales increases) and about what works (SKOS, SHACL, OntoClean, Reasoning) and what doesnt work or isnt needed (OWL, Top level ontologies, big data).
Peter Clark of the Allen Institute for AI gave my favorite talk on Structured Reasoning with Language. He discussed their research on Knowledge Graphs and reasoning but also on Belief Graphs, that consist of atomic statements with textual entailment relations. LLMs can be used to ‘reason’ over such Belief Graphs for for example explaining decisions or search results.
Main Conference
The main conference had many interesting talks in all the tracks. The industry track and resource track were quite competitive this year. In terms of quality and number of submissions, they seemed equal to the research track to me this year. Also, the special track on LLMs for Knowledge Engineering was a great success.
I was a bit hesitant with respect to this clear theme of the conference, fearing lots of “we did LLM” talks, but that was not the case at all. Most papers showed genuine interest in the strength and weaknesses of various LLMs and how they can be used in several Semantic web tasks and pipelines. There was clearly a renewed interest in methodologies (Neon, Ontology Engineering 101, Methontology etc ) and how LLMs can fit here. There were for example several talks on how LLMS can be used to generate competency questions: (“Can LLMs Generate Competency Questions? [pdf] by Youssra Rebboud et al. and “The Role of Generative AI in Competency Question Retrofitting” [pdf] by Reham Alharbi et al.”).
Roderick presenting our Resource paper
Roderick van der Weerdt presented our -best Resource paper nominated- OfficeGraph: A Knowledge Graph of Office Building IoT Measurements [pdf]. Roderick did a great job presenting this nice result from the InterConnect project and it was well-received. The winner of the Resource track best paper award was however “PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips [pdf] by Nicolas Hubert et al (in my view deservedly so).
The in-use track also had very nice papers, including a quite holistic system to map the German Financial system with knowledge Graphs [pdf] by Markus Schröder et al. Oh, and I won an award 🙂
With more focus on applications, in use, resources, methods for knowledge engineering and of course LLMs, some topics seem to get less attention. Ironically, I missed both Semantics and the Web: Semantics and reasoning did not get a lot of attention in the talks I attended and most applications were about singular knowledge graphs, rather than distributed datasets. Maybe this means that we have solved most of the challenges around these two topics, but possibly it also means that these two elements are less important for actual implementation of Knowledge Graphs. It makes one wonder about the name of the conference though…
With a truly great demo and poster session (near the beach), a great dinner, really nice people and the wonderful surroundings, ESWC2024 was a great success. See you next year in Portoroz!?
The next generation of Hybrid-Human-AI researchers are here! As part of the second International Conference on Hybrid Human-Artificial Intelligence that was held in June in Munich, German, myself and Amy Loutfi of Örebro University organized a doctoral consortium. We put out a Call for Papers asking for early to late stage PhD candidates on the topic of Hybrid Human-AI research to submit their research proposals. We received 10 submissions and after a smooth peer-reviewing process we were able to invite 8 participants to the workshop in Munich.
A really nice room for a really nice symposium
The workshop started with a great keynote by Wendy Mackay of Inria, Paris-Saclay, and the Université Paris-Saclay. Wendy is a great authority on Human-Computer Interaction and the relation of that field to Artificial Intelligence and she gave a great talk about the importance of being sensitive to both ends of the AI-HCI scale.
Wendy Mackay
Next, the participants presented their research (plans) in 20 minute presentations, with plenty time for questions and discussions. We were joined by multiple members of the community who provided interesting comments and discussion items after the talks. Each presenter was paired with another participant who would lead the discussion following the presentation. All in all my impression was that this set-up lead to a fruitful and nice atmosphere for in-depth discussions about the research.
The participants of the Doctoral Consortium (from left to right: Anastasiya Zakreuskaya, Johanna Wolff, Dhivyabharathi Ramasamy, Cosimo Palma, Regina Duarte, Victor de Boer, Wendy Mackay, Azade Farshad, Amir Homayounirad, and Nicole Orzan).
Below you find some pictures of the day. The entire programme, including (most of) the papers can be found on the HHAI conference web page. The papers are published by IOS press in the proceedings of the conference: Augmenting Human Intellect.
On behalf of Amy as well: Thank you Azade Farshad, Johanna Wolff, Regina Duarte, Amir Homayounirad, Anastasiya Zakreuskaya, Nicole Orzan, Dhivyabharathi Ramasamy, Cosimo Palma and Wendy Mackay for making the DC work. Thanks as well to the wonderful organization team of HHAI2023 to make everything run so smooth!
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