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Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space.
A machine-oriented logic based on the resolution principle
John Alan Robinson · 1965
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Shortest-path methods: Complexity, interrelations and new propositions
Stefano Pallottino · 1984
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The multi-armed bandit problem: decomposition and computation
Michael N Katehakis and Arthur F Veinott Jr · 1987
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Inductive logic programming: Theory and methods
Stephen Muggleton and Luc De Raedt · 1994
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Avoiding non-termination when learning logic programs: A case study with foil and focl
Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, and Michael Pazzani · 1994
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Refinement of datalog programs
Floriana Esposito, Angela Laterza, Donato Malerba, and Giovanni Semeraro · 1996
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The aleph manual(techical report)
Ashwin Srinivasan · 2000
Earlier work this paper cites.
Discovery of relational association rules
Luc Dehaspe and Hannu Toivonen · 2001
Earlier work this paper cites.
Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives · 2007
Earlier work this paper cites.
Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum · 2007
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
Cited alongside, same era.
Logical and relational learning
Luc De Raedt · 2008
Cited alongside, same era.
A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
Cited alongside, same era.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Cited alongside, same era.
Amie: association rule mining under incomplete evidence in ontological knowledge bases
Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek · 2013
Cited alongside, same era.
Knowledge vault: A web-scale approach to probabilistic knowledge fusion
Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang · 2014
Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum · 2018
Later among the works it cites.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
Later among the works it cites.
Knowledge graph embedding with iterative guidance from soft rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo · 2018
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Multi-hop knowledge graph reasoning with reward shaping
Xi Victoria Lin, Richard Socher, and Caiming Xiong · 2018
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Fine-grained evaluation of rule- and embedding-based systems for knowledge graph completion
Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, and Heiner Stuckenschmidt · 2018
Later among the works it cites.
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Cited alongside, same era.
Fast rule mining in ontological knowledge bases with AMIE+
Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M Suchanek · 2015
Cited alongside, same era.
Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek · 2015
Cited alongside, same era.
Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
Cited alongside, same era.
Deeppath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang · 2017
Cited alongside, same era.
Fine-grained evaluation of rule-and embedding-based systems for knowledge graph completion
Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, and Heiner Stuckenschmidt · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Later among the works it cites.
Anytime bottom-up rule learning for knowledge graph completion
Christian Meilicke, Melisachew Wudage Chekol, Daniel Ruffinelli, and Heiner Stuckenschmidt · 2019
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Knowledge graph embedding for link prediction: A comparative analysis, 2020
Andrea Rossi, Donatella Firmani, Antonio Matinata, Paolo Merialdo, and Denilson Barbosa · 2020
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You can teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla · 2020
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