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<div class="home"><h1 id="driving-scientific-discovery-through-machine-intelligence">Driving Scientific Discovery through Machine Intelligence</h1>
<blockquote>
<p>We’re developing an integrated knowledge graph for research. This model allows machines to identify and understand insights in unstructured data, such as articles, compared to today’s information about it.</p>
</blockquote>
<iframe style="display:block;" width="560" height="315" src="https://www.youtube-nocookie.com/embed/qrPDNhqxPqc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
<p>The Discovery Lab studies technology, infrastructure and methods to develop intelligent services for researchers, focusing on finding and interpreting scientific literature, to formulate hypotheses, and to interpret data. The lab operates at the crossroads of Knowledge Representation, Machine Learning and Natural Language Processing. We are advancing the ability to construct, use and study large-scale research knowledge graphs that integrate knowledge across heterogeneous scientific content and data. This will allow for a deeper, richer use of content and data across a larger span of domains than possible thus far, and enables us to grow the knowledge graph faster and more reliably, and provide better recommendations, more contextual question answering, more successful query construction, and the automatic generation of hypotheses. In other words: to drive scientific discovery using machine intelligence.</p>
<p>The Discovery Lab is an <a href="https://icai.ai/">ICAI</a> lab, funded by Elsevier, Vrije Universiteit Amsterdam and the University of Amsterdam, and supported by a PPS (Publiek-Private Samenwerking) subsidy from the Dutch government.</p>
<!--<h2 class="post-list-heading">News</h2>
<ul class="post-list"><li><span class="post-meta">Dec 15, 2023</span>
<h3>
<a class="post-link" href="/2023/12/15/The-DiscoveryLab-is-at-NeurIPS2023.html">
DiscoveryLab members attending and presenting work at NeurIPS2023!
</a>
</h3><p>Dimitrios Alivanistos, Daniel Daza, and Michael Cochez of the Discovery Lab attended NeurIPS2023 in New Orleans!</p>
<p>Excellent opportunity for inspiration, networking and promoting the work done at the Lab.</p>
</li><li><span class="post-meta">Dec 7, 2023</span>
<h3>
<a class="post-link" href="/2023/12/07/BioBLP-gets-published-in-the-Journal-of-Biomedical-Semantics.html">
New journal publication: 'BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs'
</a>
</h3><p>After almost a year of extensive research, BioBLP is published! We explore the combination of multimodal pretrained attribute encoders with Knowledge Graph Embeddings for biomedical Link Prediction!</p>
<p>Congrats to Daniel Daza, Dimitrios Alivanistos, Thom Pijnenburg, Payal Mitra, Michael Cochez and Paul Groth!</p>
</li><li><span class="post-meta">Nov 28, 2023</span>
<h3>
<a class="post-link" href="/2023/11/28/DiscoveryLab-members-organizing-the-local-LoG-conference.html">
DiscoveryLab members hosting the local Amsterdam meet-up for Learning on Graphs 2023!
</a>
</h3><p>Thom Pijnenburg, Dimitrios Alivanistos, Daniel Daza, and Michael Cochez of the Discovery Lab alongside a team of colleagues from the VU university
organized the local Amsterdam meet-up of the global Learning on Graphs conference <a href="https://logams.github.io/">LoGAMS</a>!</p>
<p>It took place in the Elsevier headquarters in Radarweg and attracted researchers working on graphs and machine learning!</p>
</li><li><span class="post-meta">Sep 13, 2023</span>
<h3>
<a class="post-link" href="/2023/09/13/DiscoveryLab-manager-gives-keynote.html">
DiscoveryLab academic manager gave a keynote at the DL4LD workshop
</a>
</h3><p>Michael Cochez, academic lab manager of the discovery lab was invited to give a keynote at the <a href="http://dl4ld2023.mruni.eu/">3rd Workshop DL4LD: Addressing Deep Learning, Relation Extraction, and Linguistic Data</a></p>
</li><li><span class="post-meta">Aug 30, 2022</span>
<h3>
<a class="post-link" href="/2022/08/30/DiscoveryLab-members-win-LM-KBC-2022.html">
DiscoveryLab members win the LM-KBC competition!
</a>
</h3><p>Dimitrios Alivanistos and Michael Cochez of the Discovery Lab alongside a team of colleagues from the university win the <a href="https://iswc2022.semanticweb.org/">ISWC2022</a> <a href="https://lm-kbc.github.io/">LM-KBC</a> competition!</p>
</li><li><span class="post-meta">Aug 29, 2022</span>
<h3>
<a class="post-link" href="/2022/08/29/Masoud-Mansoury-organizes-a-workshop-at-RecSys.html">
Masoud Mansoury organizes MORS@RecSys2022
</a>
</h3><p>After a sucessful edition <a href="https://sites.google.com/view/mors-workshop/home">last year</a> Masoud Mansoury, who is a researcher from the discovery lab, will organize the 2nd Workshop on Multi-Objective Recommender Systems at the RecSys conference. Check the homepage of the workshop <a href="https://sites.google.com/view/mors-2022/home">MORS@RecSys2022</a> for more information.</p>
</li><li><span class="post-meta">Apr 4, 2022</span>
<h3>
<a class="post-link" href="/2022/04/04/Approximate-query-answerring-ICTOpen.html">
The discovery lab at ICT.Open 2022!
</a>
</h3><p>At the <a href="https://www.ictopen.nl/">ICT.Open 2022</a> event, we will be presenting our work on approximate query answerring.
Many interesting quesitons can be formulated as graph queries.
However, in some cases, the graph does not have all the information to answer them.
In this line of work we use machine learning to still find the best answer.</p>
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/mF94YDg4KPE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
<p>We will also present a general overview of our work in the lab.</p>
<iframe style="display:block;" width="560" height="315" src="https://www.youtube-nocookie.com/embed/qrPDNhqxPqc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
</li><li><span class="post-meta">Feb 14, 2022</span>
<h3>
<a class="post-link" href="/2022/02/14/paper-accepted-at-ICLR2022.html">
Paper accepted at ICLR2022!
</a>
</h3><p>For the second year straight, the Discovery Lab will be at ICLR! This time members of the lab will present StarQE: Query Answering on hyper-relational KG’s.</p>
</li><li><span class="post-meta">Nov 20, 2021</span>
<h3>
<a class="post-link" href="/2021/11/20/knowledge-graphs-book-published.html">
Knowledge Graphs Book published
</a>
</h3><p>In a broad collaboration with top researchers in the field, Michael Cochez, academic manager of the discovery lab worked towards a comprehensive book which covers many aspects of Knowledge Graphs
The book is available from <a href="https://kgbook.org/">https://kgbook.org/</a> and in print from <a href="https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1683">Morgan&Claypool</a></p>
</li><li><span class="post-meta">Aug 20, 2021</span>
<h3>
<a class="post-link" href="/2021/08/20/Best-Task-paper-award-SemEval-2021.html">
Best Task Paper Award at SemEval 2021 – 'MeasEval - Extracting Counts and Measurements and their Related Contexts'
</a>
</h3><p>Corey Harper et. al. won the best task paper award at SemEval 2021!
See <a href="https://semeval.github.io/SemEval2021/awards">https://semeval.github.io/SemEval2021/awards</a> for the conference organizer’s writeup on it, and <a href="https://aclanthology.org/2021.semeval-1.38.pdf">https://aclanthology.org/2021.semeval-1.38.pdf</a> for the paper itself.</p>
</li><li><span class="post-meta">Jul 1, 2021</span>
<h3>
<a class="post-link" href="/2021/07/01/Masoud-Mansoury-joins.html">
Masoud Mansoury joins the Discovery Lab
</a>
</h3><p>We are happy to announce that starting Masoud Mansoury is joing the Discovery Lab from July 2021 onwards.
He will be working on Reinforcement learning over structured multi-modal information & Bias in Recommendation</p>
</li><li><span class="post-meta">May 25, 2021</span>
<h3>
<a class="post-link" href="/2021/05/25/Filling-in-the-blanks-of-KGs.html">
Filling in the blanks of knowledge graphs -- article on elsevier connect
</a>
</h3><p>After receiving an <a href="https://iclr-conf.medium.com/announcing-iclr-2021-outstanding-paper-awards-9ae0514734ab"><em>Outstanding Paper Award</em> for our paper on “Complex Query Answering with Neural Link Predictors”</a> at ICLR 2021,
people got curious how we work together with Elsevier to get our innovations into their products.</p>
<p>In an <a href="https://www.elsevier.com/connect/filling-in-the-blanks-of-knowledge-graphs">Elsevier Connect article</a> article by Alison Bert, lab members Daniel Daza and Michael Cochez, shed some light on the work done in the paper and how the synergy between the lab and Elsevier works from an academic perspective. Georgios Tsatsaronis and Anita de Waard, VP for Data Science and Research Content Operations and VP of Research Collaborations at Elsevier, respectively, elaborate on the company perspective.</p>
<p>Read the Elsevier connect article <a href="https://www.elsevier.com/connect/filling-in-the-blanks-of-knowledge-graphs">here https://www.elsevier.com/connect/filling-in-the-blanks-of-knowledge-graphs</a>
and the ICLR paper <a href="https://openreview.net/pdf?id=Mos9F9kDwkz">here https://openreview.net/pdf?id=Mos9F9kDwkz</a></p>
</li><li><span class="post-meta">Mar 31, 2021</span>
<h3>
<a class="post-link" href="/2021/03/31/Complex-Query-Answering-with-Neural-Link-Predictors-Award.html">
Outstanding paper award at ICLR 2021: 'Complex Query Answering with Neural Link Predictors'
</a>
</h3><p>Our paper “Complex Query Answering with Neural Link Predictors” has received an <em>Outstanding Paper Award</em>.</p>
<p>Out of the 860 papers of the ICLR program, only 8 selected papers, that have been deemed of exceptional quality, have received this recognition! More information can be found in
the <a href="https://iclr-conf.medium.com/announcing-iclr-2021-outstanding-paper-awards-9ae0514734ab">official ICLR announcement</a>. The actual paper can be found on openreview: <a href="https://openreview.net/pdf?id=Mos9F9kDwkz">https://openreview.net/pdf?id=Mos9F9kDwkz</a>.</p>
<p>Congratulations Daniel Daza and Michael Cochez, and also to our collaborators in this work, from the UCL Center of Artificial Intelligence, Erik Arakelyan and Pasquale Minervini!</p>
<p>In this work, we show how to re-use models for 1-hop link prediction on knowledge graphs, to answer more complex queries involving larger sub-graphs.
We improve upon previous methods that require orders of magnitude more training data.</p>
<p>The paper will be presented virtually on May 6 in the Outstanding Paper conference session 00:00 - 01:00 PDT, 03:00 - 04:00 EDT, 09:00 - 10:00 CET, 15:00 - 16:00 CST.</p>
</li><li><span class="post-meta">Mar 18, 2021</span>
<h3>
<a class="post-link" href="/2021/03/18/invited-paper-to-graphkr-published.html">
Invited paper at GKR 2020 (workshop at ECAI 2020) published 'Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification'
</a>
</h3><p>Several members of the lab got invited to write a contributions to the proceedings of the GKR2020 workshop. In this paper, we propose an additional metric to be used for the evaluation of approximate knowledge graph query answering.
We also propose a graph embedding model based on axis-aligbned hyperrectangles that seems weel suited for this task.</p>
<p>The paper is availble as an open access publication from the <a href="https://link.springer.com/book/10.1007/978-3-030-72308-8">proceedings published in the Lecture Notes Artificial Intelligence</a> series.</p>
</li><li><span class="post-meta">Feb 26, 2021</span>
<h3>
<a class="post-link" href="/2021/02/26/DeepKneeExplainer-explainable-knee-osteoarthritis.html">
Paper accepted to IEEE Access - DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging
</a>
</h3><p>In collaboration with researcher from Fraunhofer, Germany and ZB MED—Information Centre for Life Sciences, Cologne, Germany, Michael Cochez from the discovery lab got a paper accepted in IEEE Access.
In this paper, we show the result of exhaustive experimentation with explainable neural network techniques applied on radiographs and Magnetic Resonance Imaging (MRI).</p>
<p>The paper is published as open access here: <a href="https://ieeexplore.ieee.org/document/9363889">https://ieeexplore.ieee.org/document/9363889</a></p>
<p>IEEE Access: M. R. Karim, J. Jiao, T. Döhmen, M. Cochez, O. Beyan, D. Rebholz-Schuhmann and S. Decker, “DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging,” in IEEE Access, vol. 9, pp. 39757-39780, 2021, doi: 10.1109/ACCESS.2021.3062493.</p>
</li><li><span class="post-meta">Feb 11, 2021</span>
<h3>
<a class="post-link" href="/2021/02/11/ICAI-Interview-with-Rinke-Hoekstra.html">
ICAI Interview with Rinke Hoekstra: ‘Academics help seeing the big picture.’
</a>
</h3><p>Rinke Hoekstra, Lead Architect at Elsevier, is Industry Director of Discovery Lab. Rinke was interviewed by ICAI to talk about the collaboration with the University of Amsterdam and Vrije Universiteit Amsterdam: ‘Within Elsevier this is already seen as one of the most successful collaborations with academic partners.’</p>
<p>Read the interview at <a href="https://icai.ai/icai-interview-with-rinke-hoekstra-academics-help-seeing-the-big-picture/">the ICAI website</a>.</p>
</li><li><span class="post-meta">Jan 18, 2021</span>
<h3>
<a class="post-link" href="/2021/01/18/Inductive-Entity-Representations-from-Text-via-Link-Prediction.html">
Paper accepted at The Web Conference: 'Inductive Entity Representations from Text via Link Prediction'
</a>
</h3><p>Our paper, “Inductive Entity Representations from Text via Link Prediction”, has been accepted at The Web Conference, 2021. With Daniel Daza, Michael Cochez and Paul Groth, we investigate how to learn representations of entities in a knowledge graph given their textual description. We then reuse these representations in tasks of entity classification and information retrieval, obtaining significant improvements over previously proposed methods.</p>
<p>A preprint of this work can be found here: <a href="https://arxiv.org/abs/2010.03496">https://arxiv.org/abs/2010.03496</a></p>
</li><li><span class="post-meta">Jan 13, 2021</span>
<h3>
<a class="post-link" href="/2021/01/13/Complex-Query-Answering-with-Neural-Link-Predictors.html">
Paper accepted at ICLR 2021: 'Complex Query Answering with Neural Link Predictors'
</a>
</h3><p>Our paper “Complex Query Answering with Neural Link Predictors” was accepted for oral presentation at ICLR 2021. It is the result of a collaboration with Erik Arakelyan and Pasquale Minervini from UCL. We show how to re-use models for 1-hop link prediction on knowledge graphs, to answer more complex queries involving larger sub-graphs. We improve upon previous methods that require orders of magnitude more training data.</p>
<p>The paper will be presented virtually in the first week of May.</p>
</li><li><span class="post-meta">Aug 26, 2020</span>
<h3>
<a class="post-link" href="/2020/08/26/Message-Passing-Query-Embedding.html">
Paper accepted at ICML 2020 GRL Workshop: 'Message Passing Query Embedding'
</a>
</h3><p>The paper, titled “Message Passing Query Embedding” was accepted at the ICML 2020 Workshop on Graph Representation and Learning. It is authored by two of our lab members: Daniel Daza and Michael Cochez. The paper proposes a novel architecture for graph embeddings of knowledge graph queries, with important advantages compared to previous works.</p>
</li><li><span class="post-meta">May 27, 2020</span>
<h3>
<a class="post-link" href="/2020/05/27/How-AI-and-Knowledge-Graphs-can-make-Research-Easier.html">
How AI and knowledge graphs can make your research easier
</a>
</h3><p>Data scientists are developing a knowledge graph with researchers in mind in Elsevier’s DiscoveryLab, collaborating with Vrije Universiteit and University of Amsterdam.</p>
<p>Read the article on <a href="https://www.elsevier.com/connect/how-ai-and-knowledge-graphs-can-make-your-research-easier">Elsevier Connect</a>.</p>
</li></ul>
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