AKSW Colloquium, 18.07.2016, AEGLE and node2vec

On Monday 18.07.2016, Kleanthi Georgala will give her Colloquium presentation for her paper “An Efficient Approach for the Generation of Allen Relations”, that was accepted at the European Conference on Artificial Intelligence (ECAI) 2016.


Event data is increasingly being represented according to the Linked Data principles. The need for large-scale machine learning on data represented in this format has thus led to the need for efficient approaches to compute RDF links between resources based on their temporal properties. Time-efficient approaches for computing links between RDF resources have been developed over the last years. However, dedicated approaches for linking resources based on temporal relations have been paid little attention to. In this paper, we address this research gap by presenting AEGLE, a novel approach for the efficient computation of links between events according to Allen’s interval algebra. We study Allen’s relations and show that we can reduce all thirteen relations to eight simpler relations. We then present an efficient algorithm with a complexity of O(n log n) for computing these eight relations. Our evaluation of the runtime of our algorithms shows that we outperform the state of the art by up to 4 orders of magnitude while maintaining a precision and a recall of 1.

Tommaso SoruAfterwards, Tommaso Soru will present a paper considered the latest chapter of the Everything-2-Vec saga, which encompasses outstanding works such as Word2Vec and Doc2Vec. The paper title is node2vec: Scalable Feature Learning for Networks” [PDF] by Aditya Grover and Jure Leskovec, accepted for publication at the International Conference on Knowledge Discovery and Data Mining (KDD), 2016 edition.

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