I am an Associate Professor (UHD) at the User-Centric Data Science group at the Computer Science department of the Vrije Universiteit Amsterdam (VU) I am also a co-director of the Cultural AI Lab. In my research, I combine (Semantic) Web technologies with Human-Computer Interaction, Knowledge Representation and Information Extraction to tackle research challenges in various domains. These include Cultural Heritage, Digital Humanities and ICT for Development (ICT4D). I am currently involved in the following research projects:
HEDGE-IoT (2024-2027). HEDGE-IoT is a HorizonEurope project that proposes a novel Digital Framework which aims to deploy IoT assets at different levels of the energy system. We collaborate with TNO and Arnhems Buiten on IoT data conversion and enrichment; user-centric and explainable machine learning.
PressingMatter: Our role in this NWO project is about developing data models to support societal reconciliation with the colonial past and its afterlives.
HAICu: In this NWO project we investigate perspective-aware AI to make digital heritage collections more accessible.
Hybrid Intelligence: Augmenting Human Intellect. In this project, I am involved in various sub-projects:
Mapping Missionaries is a Network Institute Academy Assistant project that seeks to improve access to information about Dutch missionaries in the former colonies.
For other and older research projects, see the “research” tab.
From April 7th to 12th, 2025, I had the pleasure of visiting Kuching, Sarawak, alongside my colleague Lea Krause and Master’s student Eva Heemskerk from Vrije Universiteit Amsterdam. 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.
We kicked off our trip at SMK Agama Tun Ahmad Zaidi, where Lea introduced Form 3 students to the fascinating world of Natural Language Processing and Large Language Models using Hugging Face tools. The students experimented with sentiment analysis and chatbots—many for the first time.
Lea Krause assisting students with LLMs (photo by Celine Haren and Cheah Wai Shiang)
Later that day, I delivered a lecture at UNIMAS on “Knowledge Graphs for Cultural AI,” emphasizing how cultural context can shape ethical and inclusive AI systems. During the trip, we also managed to do some data gathering about colonial cultural heritage, by presenting participants with objects from the Sarawak region and asking their perspective on objects. This is done in the context of our HAICu project.
At Sarawak Skills, I presented on embedding AI into technical and vocational education, followed by a thoughtful roundtable on how educators can guide students to use (Hybrid) AI critically.
The discussion panel at Sarawak Skills (photo: Celine Haren and Cheah Wai Shiang)
On our final day, we visited SK Muhibbah, a rural primary school, where we shared stories about the Netherlands and engaged with young students whose curiosity and enthusiasm reminded us of AI’s potential to reach and inspire across all communities.
The three of us with school teachers and ms. Nurfauza, dr. Cheah and Celine
That afternoon, we also met with the minister of Education and the director of the new Sarawak AI institute to explore possible cooperation between VU, UNIMAS and the institute.
This visit was a truly enriching experience, made possible by the ERASMUS+ funding scheme. We very much look forward to deepening our collaborations in the region.
In the article, we describe the principles and technologies of Linked (Open) Data and how these have been applied in the heritage domain. We also include a section on LOD for Colonial Heritage, matching some of the work we are currently doing in the Pressing Matter and HAICu projects.
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
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
[This post is based on the Bachelor Project AI of Annriya Binoy]
In her bachelor thesis “Evaluating Methodologies for Information Extraction and Knowledge Graph Creation from Handwritten Historical Documents”, Annriya Binoy provides a systematic evaluation of various methodologies for extracting and structuring information from historical handwritten documents, with the goal of identifying the most effective strategies.
As a case study, the research investigates several methods on scanned pages from the National Archive of the Netherlands, specifically the service records and pension registers of the late 18th century and early 19th century of the Koninklijk Nederlands Indisch Leger (KNIL), see the example below. The task was defined as that of extracting birth events.
Four approaches are analyzed:
Handwritten Text Recognition (HTR) using the Transkribus tool
a combination of Large Language Models (LLM) and Regular Expressions (Regex),
Regex alone
Fuzzy Search
HTR and the LLM-Regex combination show strong performance and adaptability with F1 measure values of 0.88. While Regex alone delivers high accuracy, it lacks comprehensiveness. Fuzzy Search proves effective in handling transcription errors common in historical documents, offering a balance between accuracy and robustness. This research offers initial but practical solutions for the digitization and semantic enrichment of historical archives, and it also addresses the challenges of preserving contextual integrity when constructing knowledge graphs from extracted data.
More details can be found in Annriya’s thesis below.
[This post is based on Nour al Assali‘s bachelor AI thesis]
Nour’s research explores the use of Natural Language Processing (NLP) and Knowledge Graphs to investigate the historical connections and cultural exchanges within global cuisines. The thesis “Flavours of History: Exploring Historical and Cultural Connections Through Ingredient Analysis Using NLP and Knowledge Graphs” describes a method for analyzing ingredient usage patterns across various cuisines by processing a dataset of recipes. Its goal is to trace the diffusion and integration of ingredients into different culinary traditions. The primary aim is to establish a digital framework for addressing questions related to culinary history and cultural interactions.
The methodology involves applying NLP to preprocess recipe data, focusing on extracting and normalizing ingredient names. The pipeline contains steps for stop word removal, token- and lemmatization, character replacements etc.
With the results, a Knowledge Graph is constructed to map relationships between ingredients, recipes, and cuisines. The approach also includes visualizing these connections, with an interactive map and other tools designed to provide insights into the data and answer key research questions. The figure below shows a visualisation of top ingredients per cuisine.
Case studies on ingredients such as pistachios, tomatoes, basil, olives, and cardamom illustrate distinct usage patterns and origins. The findings reveal that certain ingredients—like pistachios, basil, and tomatoes—associated with specific regions have gained widespread international popularity, while others, such as olives and cardamom, maintain strong ties to their places of origin. This research underscores the influence of historical trade routes and cultural exchanges on contemporary culinary practices and offers a digital foundation for future investigations into culinary history and food culture.
The code and dataset used in this research are available on GitHub: https://github.com/Nour-alasali/BPAI. The complete thesis can be found below.
[This blog post is based on Jesse van Haaster‘s bachelor thesis Artificial Intelligence at VU]
Knowledge Graphs represent data as triples, connecting related data points. This form of representation is widely used for various applications, such as querying information and drawing inferences from data. For fine-tuning such applications, actual KGs are needed. However, in certain domains like medical records or smart home devices, creating large-scale public knowledge graphs is challenging due to privacy concerns. To address this, generating synthetic knowledge graph data that mimics the original while preserving privacy is highly beneficial.
Jesse’s thesis explored the feasibility of generating meaningful synthetic time series data for knowledge graphs. He specifically does this in the smart building / IoT domain, building on our previous work on IoT knowledge graphs, including OfficeGraph.
To this end, two existing generative adversarial networks (GANs), CTGAN and TimeGAN, are evaluated for their ability to produce synthetic data that retains key characteristics of the original OfficeGraph dataset. Jesse compared among other things the differences in distributions of values for key features, such as humidity, temperature and co2 levels, seen below.
Key value distributions for CTGAN-generated data vs original data
Key value distributions for TimeGAN-generated data vs original data
The experiment results indicate that while both models capture some important features, neither is able to replicate all of the original data’s properties. Further research is needed to develop a solution that fully meets the requirements for generating meaningful synthetic knowledge graph data.
For deep and meaningful integration of AI tools in the Digital Humanities (DH) discipline, Hybrid Intelligence (HI) as a research paradigm. In DH research, the use of digital methods and specifically that of Artificial Intelligence is subject to a set of requirements and constraints. In our position paper, which we presented at the HHAI2024 conference in Malmö, we argue that these are well-supported by the capabilities and goals of HI. Our paper includes the identification of five such DH requirements: Successful AI systems need to be able to
collaborate with the (human) scholar;
support data criticism;
support tool criticism;
be aware of and cater to various perspectives and
support distant and close reading.
In our paper, we take the CARE principles of Hybrid Intelligence (collaborative, adaptive, responsible and explainable) as theoretical framework and map these to the DH requirements. In this mapping, we include example research projects. We finally address how insights from DH can be applied to HI and discuss open challenges for the combination of the two disciplines.
You can find the paper here: Victor de Boer and Lise Stork. “Hybrid Intelligence for Digital Humanities.” HHAI 2024: Hybrid Human AI Systems for the Social Good. pp. 94-104. Frontiers in Artificial Intelligence and Applications. Vol. 386. IOS Press. DOI: 10.3233/FAIA240186
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!?
There is more than one side to every story. This common saying is not only true for works of fiction. In the global data space that is the Semantic Web, views and perspectives from different people, organizations and cultures should be available. I identify three challenges towards such a polyvocal Semantic Web. I will talk about ways to identify various voices, to model different perspectives and to make these perspectives available to end users. I will give examples from the cultural heritage domain, both in how semantic technologies can be of use to make available various perspectives on people, objects and events there but also how insights from the domain can help to shape the polyvocal Semantic Web.
I am an Associate Professor (UHD) at the User-Centric Data Science group at the Computer Science department of the Vrije Universiteit Amsterdam (VU) I am also a co-director of the Cultural AI Lab. In my research, I combine (Semantic) Web technologies with Human-Computer Interaction, Knowledge Representation and Information Extraction to tackle research challenges in various domains. These include Cultural Heritage, Digital Humanities and ICT for Development (ICT4D). I am currently involved in the following research projects:
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