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1,049 results on '"Sack, Harald"'

51. Exploring Large Movie Collections: Comparing Visual Berrypicking and Traditional Browsing

Author

Low, Thomas, Hentschel, Christian, Stober, Sebastian, Sack, Harald, Nürnberger, Andreas, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Amsaleg, Laurent, editor, Guðmundsson, Gylfi Þór, editor, Gurrin, Cathal, editor, Jónsson, Björn Þór, editor, and Satoh, Shin’ichi, editor

Published
2017
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55. Epilogue

Author

Meinel, Christoph, Sack, Harald, Meinel, Christoph, and Sack, Harald

Published
2014
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60. Prologue

Author

Meinel, Christoph, Sack, Harald, Meinel, Christoph, and Sack, Harald

Published
2014
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68. Quantitative Analysis of Art Market Using Ontologies, Named Entity Recognition and Machine Learning: A Case Study

Author

Filipiak, Dominik, Agt-Rickauer, Henning, Hentschel, Christian, Filipowska, Agata, Sack, Harald, van der Aalst, Wil M.P., Series editor, Mylopoulos, John, Series editor, Rosemann, Michael, Series editor, Shaw, Michael J., Series editor, Szyperski, Clemens, Series editor, Abramowicz, Witold, editor, Alt, Rainer, editor, and Franczyk, Bogdan, editor

Published
2016
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69. TIB|AV-Portal: Integrating Automatically Generated Video Annotations into the Web of Data

Author

Waitelonis, Jörg, Plank, Margret, Sack, Harald, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fuhr, Norbert, editor, Kovács, László, editor, Risse, Thomas, editor, and Nejdl, Wolfgang, editor

Published
2016
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70. I am a Machine, Let Me Understand Web Media!

Author

Knuth, Magnus, Waitelonis, Jörg, Sack, Harald, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bozzon, Alessandro, editor, Cudre-Maroux, Philippe, editor, and Pautasso, Cesare, editor

Published
2016
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71. Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Short Paper)

Author

Knuth, Magnus, Hartig, Olaf, Sack, Harald, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Debruyne, Christophe, editor, Panetto, Hervé, editor, Meersman, Robert, editor, Dillon, Tharam, editor, Kühn, eva, editor, O'Sullivan, Declan, editor, and Ardagna, Claudio Agostino, editor

Published
2016
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72. MADLINK: Attentive multihop and entity descriptions for link prediction in knowledge graphs.

Author

Biswas, Russa, Sack, Harald, and Alam, Mehwish

Subjects
KNOWLEDGE graphs, RANDOM walks, FORECASTING, TRAILS
Abstract

Knowledge Graphs (KGs) comprise of interlinked information in the form of entities and relations between them in a particular domain and provide the backbone for many applications. However, the KGs are often incomplete as the links between the entities are missing. Link Prediction is the task of predicting these missing links in a KG based on the existing links. Recent years have witnessed many studies on link prediction using KG embeddings which is one of the mainstream tasks in KG completion. To do so, most of the existing methods learn the latent representation of the entities and relations whereas only a few of them consider contextual information as well as the textual descriptions of the entities. This paper introduces an attentive encoder-decoder based link prediction approach considering both structural information of the KG and the textual entity descriptions. Random walk based path selection method is used to encapsulate the contextual information of an entity in a KG. The model explores a bidirectional Gated Recurrent Unit (GRU) based encoder-decoder to learn the representation of the paths whereas SBERT is used to generate the representation of the entity descriptions. The proposed approach outperforms most of the state-of-the-art models and achieves comparable results with the rest when evaluated with FB15K, FB15K-237, WN18, WN18RR, and YAGO3-10 datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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73. Divided by the Algorithm? The (Limited) Effects of Content- and Sentiment-Based News Recommendation on Affective, Ideological, and Perceived Polarization.

Author

Ludwig, Katharina, Grote, Alexander, Iana, Andreea, Alam, Mehwish, Paulheim, Heiko, Sack, Harald, Weinhardt, Christof, and Müller, Philipp

Subjects
AFFECT (Psychology), RECOMMENDER systems, SEARCH engines, ALGORITHMS, POLARIZATION (Social sciences), USER-generated content
Abstract

Recent rises in political polarization across the globe are often ascribed to algorithmic content filtering on social media, news platforms, or search engines. The widespread usage of news recommendation systems (NRS) is theorized to drive users in homogenous information environments and, thereby, drive affective, ideological, and perceived polarization. To test this assumption, we conducted an online experiment (n = 750) with running algorithms that enriches content-based NRS with negative or neutral sentiment. Our experiment finds only limited evidence for polarization effects of content-based NRS. Nevertheless, the time spent with an NRS and its recommended articles seems to play a crucial role as a moderator of polarization. The longer participants were using an NRS enriched with negative sentiment, the more they got affectively polarized, whereas participants using an NRS incorporating balanced sentiment ideologically depolarized over time. Implications for future research are discussed. [ABSTRACT FROM AUTHOR]

Published
2023
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83. Prologue

Author

Meinel, Christoph, Sack, Harald, Meinel, Christoph, and Sack, Harald

Published
2013
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86. Epilogue

Author

Meinel, Christoph, Sack, Harald, Meinel, Christoph, and Sack, Harald

Published
2013
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88. If We Did Not Have ImageNet: Comparison of Fisher Encodings and Convolutional Neural Networks on Limited Training Data

Author

Hentschel, Christian, Wiradarma, Timur Pratama, Sack, Harald, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Pavlidis, Ioannis, editor, Feris, Rogerio, editor, McGraw, Tim, editor, Elendt, Mark, editor, Kopper, Regis, editor, Ragan, Eric, editor, Ye, Zhao, editor, and Weber, Gunther, editor

Published
2015
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89. What Image Classifiers Really See – Visualizing Bag-of-Visual Words Models

Author

Hentschel, Christian, Sack, Harald, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, He, Xiangjian, editor, Luo, Suhuai, editor, Tao, Dacheng, editor, Xu, Changsheng, editor, Yang, Jie, editor, and Hasan, Muhammad Abul, editor

Published
2015
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97. Prolog

Author

Meinel, Christoph, Sack, Harald, Meinel, Christoph, and Sack, Harald

Published
2012
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99. Epilog

Author

Meinel, Christoph, Sack, Harald, Meinel, Christoph, and Sack, Harald

Published
2012
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100. Knowledge Graph Embeddings: Open Challenges and Opportunities

Author

Russa Biswas and Lucie-Aimée Kaffee and Michael Cochez and Stefania Dumbrava and Theis E. Jendal and Matteo Lissandrini and Vanessa Lopez and Eneldo Loza Mencía and Heiko Paulheim and Harald Sack and Edlira Kalemi Vakaj and Gerard de Melo, Biswas, Russa, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, de Melo, Gerard, Russa Biswas and Lucie-Aimée Kaffee and Michael Cochez and Stefania Dumbrava and Theis E. Jendal and Matteo Lissandrini and Vanessa Lopez and Eneldo Loza Mencía and Heiko Paulheim and Harald Sack and Edlira Kalemi Vakaj and Gerard de Melo, Biswas, Russa, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, and de Melo, Gerard

Abstract

While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph completion and similarity search. This study surveys advances as well as open challenges and opportunities in this area. For instance, the most prominent embedding models focus primarily on structural information. However, there has been notable progress in incorporating further aspects, such as semantics, multi-modal, temporal, and multilingual features. Most embedding techniques are assessed using human-curated benchmark datasets for the task of link prediction, neglecting other important real-world KG applications. Many approaches assume a static knowledge graph and are unable to account for dynamic changes. Additionally, KG embeddings may encode data biases and lack interpretability. Overall, this study provides an overview of promising research avenues to learn improved KG embeddings that can address a more diverse range of use cases.

Published
2023
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