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‎2020/05/27/How-AI-and-Knowledge-Graphs-can-make-Research-Easier.html‎

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<meta name="description" content="Data scientists are developing a knowledge graph with researchers in mind in Elsevier’s DiscoveryLab, collaborating with Vrije Universiteit and University of Amsterdam. Read the article on Elsevier Connect." />
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‎2020/08/26/Message-Passing-Query-Embedding.html‎

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<meta name="description" content="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." />
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<meta property="og:description" content="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." />
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‎2021/01/13/Complex-Query-Answering-with-Neural-Link-Predictors.html‎

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<meta name="description" content="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. The paper will be presented virtually in the first week of May." />
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<meta property="og:description" content="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. The paper will be presented virtually in the first week of May." />
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‎2021/01/18/Inductive-Entity-Representations-from-Text-via-Link-Prediction.html‎

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<meta name="description" content="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. A preprint of this work can be found here: https://arxiv.org/abs/2010.03496" />
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