TY - CONF AB - Link discovery plays a key role in the integration and use of data across RDF knowledge graphs. Active learning approaches are a common family of solutions to address the problem of learning how to compute links from users. So far, only active learning from perfect oracles has been considered in the literature. However, real oracles are often far from perfect (e.g., in crowdsourcing). We hence study the problem of learning how to compute links across knowledge graphs from noisy oracles, i.e., oracles that are not guaranteed to return correct classification results. We present a novel approach for link discovery based on a probabilistic model, with which we estimate the joint odds of the oracles’ guesses. We combine this approach with an iterative learning approach based on refinements. The resulting method, Ligon, is evaluated on 10 benchmark datasets. Our results suggest that Ligon configured with 10 iterations and 10 training examples per iteration achieves more than 95% of the F-measure achieved by state-of-the-art algorithms trained with a perfect oracle. Moreover, Ligon outperforms batch learning approaches devised to be trained with small amounts of training data by more than 40% F-measure on average. AU - Sherif, Mohamed AU - Dreßler}, Kevin AU - Ngonga Ngomo, Axel-Cyrille ID - 29010 KW - 2020 dice simba sherif ligon ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal kevin T2 - Proceedings of Ontology Matching Workshop 2020 TI - LIGON – Link Discovery with Noisy Oracles ER - TY - JOUR AU - Bigerl, Alexander AU - Conrads, Felix AU - Behning, Charlotte AU - Sherif, Mohamed AU - Saleem, Muhammad AU - Ngonga Ngomo, Axel-Cyrille ID - 29039 JF - The Semantic Web -- ISWC 2020 KW - sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba sys:relevantFor:limbo sys:relevantFor:raki daikiri speaker tentris knowgraphs bigerl fconrads saleem sherif ngonga group_aksw dice TI - Tentris – A Tensor-Based Triple Store ER - TY - CONF AU - Zahera, Hamada Mohamed Abdelsamee AU - Sherif, Mohamed ID - 29040 KW - 2020 dice zahera sherif knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal T2 - Proceedings of Mining the Web of HTML-embedded Product Data Workshop (MWPD2020) TI - ProBERT: Product Data Classification with Fine-tuning BERT Model ER - TY - CONF AB - Modern data-driven frameworks often have to process large amounts of data periodically. Hence, they often operate under time or space constraints. This also holds for Linked Data-driven frameworks when processing RDF data, in particular, when they perform link discovery tasks. In this work, we present a novel approach for link discovery under constraints pertaining to the expected recall of a link discovery task. Given a link specification, the approach aims to find a subsumed link specification that achieves a lower run time than the input specification while abiding by a predefined constraint on the expected recall it has to achieve. Our approach, dubbed LIGER, combines downward refinement oper- ators with monotonicity assumptions to detect such specifications. We evaluate our approach on seven datasets. Our results suggest that the different implemen- tations of LIGER can detect subsumed specifications that abide by expected recall constraints efficiently, thus leading to significantly shorter overall run times than our baseline. AU - Georgala, Kleanthi AU - Sherif, Mohamed AU - Ngonga Ngomo, Axel-Cyrille ID - 29007 KW - 2020 dice simba sherif hecate ngonga knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai sys:relevantFor:bis limes limbo opal georgala T2 - Proceedings of Ontology Matching Workshop 2020 TI - LIGER – Link Discovery with Partial Recall ER - TY - CHAP AU - Georgi, Christopher ED - Kämper, Heidrun ED - Warnke, Ingo H. ID - 46564 T2 - Diskurs – ethisch TI - „Wir müssen die Sorgen der Menschen ernst nehmen“ – Zur sprachlichen Thematisierung von Sorge und Angst in der Politik ER - TY - GEN AB - Theoretical papers show that optimal prevention decisions in the sense of selfprotection (i.e., primary prevention) depend not only on the level of (second-order) risk aversion but also on higher-order risk preferences such as prudence (third-order risk aversion). We study empirically whether these theoretical results hold and whether prudent individuals show less preventive (self-protection) effort than non-prudent individuals. We use a unique dataset that combines data on higher-order risk preferences and various measures of observed real-world prevention behavior. We find that prudent individuals indeed invest less in self-protection as measured by influenza vaccination. This result is driven by high risk individuals such as individuals >60 years of age or chronically ill. We do not find a clear empirical relationship between riskpreferences and prevention in the sense of self-insurance (i.e. secondary prevention). Neither risk aversion nor prudence is related to cancer screenings such as mammograms, Pap smears or X-rays of the lung. AU - Mayrhofer, Thomas AU - Schmitz, Hendrik ID - 46541 KW - prudence KW - risk preferences KW - prevention KW - vaccination KW - screening TI - Prudence and prevention: Empirical evidence VL - 863 ER - TY - JOUR AU - Schwabl, Franziska AU - Janssen, Elmar AU - Sloane, Peter F. E. ID - 46581 IS - 1 JF - Zeitschrift für Berufs- und Wirtschaftspädagogik KW - Applied Mathematics KW - General Mathematics SN - 0172-2875 TI - Sprachsensible Formulierung von Erhebungsinstrumenten VL - 116 ER - TY - CHAP AU - Ried, Dennis ID - 46585 SN - 9783662625057 T2 - Musik in Baden-Württemberg. Jahrbuch 2019/20 TI - »auch auf gesanglichem Gebiete überaus thätig«. Max Reger und das Lied ER - TY - CONF AU - Garnefeld, I. AU - Krah, T. AU - Böhm, Eva AU - Gremler, D. D. ID - 46689 T2 - 2020 AMA Winter Academic Conference, San Diego, CA TI - Do product testing programs lead to more favorable online reviews? (ausgezeichnet mit Best Paper Award) ER - TY - CONF AU - Eggert, A. AU - Böhm, Eva AU - Akalan, R. AU - Gebauer, H. ID - 46690 T2 - 2020 AMA Winter Academic Conference, San Diego, CA TI - Manufacturers’ service growth through mergers and acquisitions – An event study ER -