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.’
Read the interview at the ICAI website.
Paper accepted at The Web Conference: 'Inductive Entity Representations from Text via Link Prediction'
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
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.
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.
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.