A system for reasoning-based link prediction in large knowledge graphs
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Wang, Z
Zhang, X
Omran, PG
Feng, Z
Wang, K
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Auckland, New Zealand
Abstract
This poster paper presents an efficient method R-Linker for link prediction in large knowledge graphs, based on rule learning. The scalability and efficiency is achieved by a combination of several optimisation techniques. Experimental results show that R-Linker is able to handle KGs with over 10 million of entities and more efficient than existing state-of-the-art methods including RLvLR and AMIE+ in rule learning stage for link prediction.
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CEUR Workshop Proceedings
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2456
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© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Artificial intelligence
Information systems
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Wu, H; Wang, Z; Zhang, X; Omran, PG; Feng, Z; Wang, K, A system for reasoning-based link prediction in large knowledge graphs, CEUR Workshop Proceedings, 2019, 2456, pp. 121-124