Multiple Interaction Attention Model for Open-World Knowledge Graph Completion

作者: Chenpeng Fu , Zhixu Li , Qiang Yang , Zhigang Chen , Junhua Fang

DOI: 10.1007/978-3-030-34223-4_40

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摘要: … In this paper, we propose a novel open-world KGC approach based on the same input resources with ConMask, ie, entity names, relationship names, and entity descriptions. But …

参考文章(29)
Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu, Modeling Relation Paths for Representation Learning of Knowledge Bases empirical methods in natural language processing. pp. 705- 714 ,(2015) , 10.18653/V1/D15-1082
Mehmet A. Orgun, Guanfeng Liu, Yan Wang, Optimal social trust path selection in complex social networks national conference on artificial intelligence. pp. 1391- 1398 ,(2010)
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick van Kleef, Sören Auer, Christian Bizer, DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia Social Work. ,vol. 6, pp. 167- 195 ,(2015) , 10.3233/SW-140134
Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Ee-Peng Lim, Finding the Optimal Social Trust Path for the Selection of Trustworthy Service Providers in Complex Social Networks IEEE Transactions on Services Computing. ,vol. 6, pp. 152- 167 ,(2013) , 10.1109/TSC.2011.58
Sepp Hochreiter, Jürgen Schmidhuber, Long short-term memory Neural Computation. ,vol. 9, pp. 1735- 1780 ,(1997) , 10.1162/NECO.1997.9.8.1735
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, Jamie Taylor, Freebase Proceedings of the 2008 ACM SIGMOD international conference on Management of data - SIGMOD '08. pp. 1247- 1250 ,(2008) , 10.1145/1376616.1376746
Ilya Sutskever, Geoffrey Hinton, Alex Krizhevsky, Ruslan Salakhutdinov, Nitish Srivastava, Dropout: a simple way to prevent neural networks from overfitting Journal of Machine Learning Research. ,vol. 15, pp. 1929- 1958 ,(2014)
Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult IEEE Transactions on Neural Networks. ,vol. 5, pp. 157- 166 ,(1994) , 10.1109/72.279181