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Neurosymbolic AI is an increasingly active area of research that combines symbolic reasoning methods with deep learning to leverage their complementary benefits.
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Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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Markus Krötzsch, Frantisek Simancik and Ian Horrocks · 2012
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“Query-Time Reasoning in Uncertain RDF Knowledge Bases with Soft and Hard Rules.”
Ndapandula Nakashole, Mauro Sozio, Fabian Suchanek and Martin Theobald · 2012
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“Translating embeddings for modeling multi-relational data”
Antoine Bordes et al · 2013
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“DeepWalk: Online Learning of Social Representations”, KDD ’14
Bryan Perozzi, Rami Al-Rfou and Steven Skiena · 2014
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“Scaling-up importance sampling for Markov logic networks”
Deepak Venugopal and Vibhav Gogate · 2014
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“Embedding entities and relations for learning and inference in knowledge bases”
Bishan Yang et al · 2014
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“Semantic data mining: A survey of ontology-based approaches”
Dejing Dou, Hao Wang and Haishan Liu · 2015
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“Fast rule mining in ontological knowledge bases with AMIE+”
Luis Galárraga, Christina Teflioudi, Katja Hose and Fabian Suchanek · 2015
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“Multimodal data fusion: an overview of methods, challenges, and prospects”
Dana Lahat, Tülay Adali and Christian Jutten · 2015
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“Injecting logical background knowledge into embeddings for relation extraction”
Tim Rocktäschel, Sameer Singh and Sebastian Riedel · 2015
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“Artificial neural network models”
Peter Tino, Lubica Benuskova and Alessandro Sperduti · 2015
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“Tensorlog: A differentiable deductive database”
William Cohen · 2016
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“The mTOR signalling cascade: paving new roads to cure neurological disease”
Peter Crino · 2016
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“node2vec: Scalable feature learning for networks”
Aditya Grover and Jure Leskovec · 2016
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“Jointly Embedding Knowledge Graphs and Logical Rules”
Shu Guo et al · 2016
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“Sparseness analysis in the pretraining of deep neural networks”
Jun Li et al · 2016
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“Neuro-symbolic representation learning on biological knowledge graphs”
Mona Alshahrani et al · 2017
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“An approach to explainable deep learning using fuzzy inference”
David Bonanno et al · 2017
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“The ChEMBL database in 2017”
Anna Gaulton et al · 2017
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“Incorporating expert knowledge into keyphrase extraction”
Sujatha Gollapalli, Xiao-Li Li and Peng Yang · 2017
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“Systematic integration of biomedical knowledge prioritizes drugs for repurposing”
Daniel Himmelstein et al · 2017
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“Improving scalability of inductive logic programming via pruning and best-effort optimisation”
Mishal Kazmi, Peter Schüller and Yücel Saygın · 2017
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“Semi-Supervised Classification with Graph Convolutional Networks”
Thomas. Kipf and Max Welling · 2017
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“Regularizing knowledge graph embeddings via equivalence and inversion axioms”
Pasquale Minervini et al · 2017
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“Scalable learning and inference in Markov logic networks”
Zhengya Sun et al · 2017
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Petar Veličković et al · 2017
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“Differentiable learning of logical rules for knowledge base reasoning”
Fan Yang, Zhilin Yang and William Cohen · 2017
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“Automatic dangerous driving intensity analysis for advanced driver assistance systems from multimodal driving signals”
Jia-Li Yin, Bo-Hao Chen, Kuo-Hua Lai and Ying Li · 2017
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“Challenges and opportunities: from big data to knowledge in AI 2.0”
Yue-ting Zhuang, Fei Wu, Chun Chen and Yun-he Pan · 2017
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“Fast and scalable learning of neuro-symbolic representations of biomedical knowledge”
Asan Agibetov and Matthias Samwald · 2018
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“Improving knowledge graph embedding using simple constraints”
Boyang Ding, Quan Wang, Bin Wang and Li Guo · 2018
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“Knowledge graph embedding with iterative guidance from soft rules”
Shu Guo et al · 2018
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“Embedding logical queries on knowledge graphs”
Will Hamilton et al · 2018
Cited alongside, same era.
“Rule learning from knowledge graphs guided by embedding models”
Vinh Ho et al · 2018
Cited alongside, same era.
“Road traffic forecasting: Recent advances and new challenges”
Ibai Lana, Javier Del, Manuel Velez and Eleni Vlahogianni · 2018
Cited alongside, same era.
“Modeling relational data with graph convolutional networks”
Michael Schlichtkrull et al · 2018
Cited alongside, same era.
“Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence”
Sandro Skansi · 2018
Cited alongside, same era.
“A Semantic Loss Function for Deep Learning with Symbolic Knowledge”
Jingyi Xu et al · 2018
Cited alongside, same era.
“Neural multi-hop reasoning with logical rules on biomedical knowledge graphs”
Yushan Liu et al · 2021
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“Learning Representations for Sub-Symbolic Reasoning”
Giuseppe Marra, Michelangelo Diligenti and Francesco Giannini · 2021
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“Neural markov logic networks”
Giuseppe Marra and Ondřej Kuželka · 2021
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“Addressing bias in big data and AI for health care: A call for open science”
Natalia Norori et al · 2021
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“SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models”
Simon Ott, Christian Meilicke and Matthias Samwald · 2021
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“Link prediction based on graph neural networks”
Muhan Zhang and Yixin Chen · 2018
Cited alongside, same era.
“Modeling polypharmacy side effects with graph convolutional networks”
Marinka Zitnik, Monica Agrawal and Jure Leskovec · 2018
Cited alongside, same era.
“The price of interpretability”
Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet and Sebastien Martin · 2019
Cited alongside, same era.
“Improved knowledge graph embedding using background taxonomic information”
Bahare Fatemi, Siamak Ravanbakhsh and David Poole · 2019
Cited alongside, same era.
“Attention based spatial-temporal graph convolutional networks for traffic flow forecasting”
Shengnan Guo et al · 2019
Cited alongside, same era.
“Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts”
Junheng Hao et al · 2019
Cited alongside, same era.
“Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models”
Benedek Rozemberczki et al · 2021
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“Neuro-symbolic artificial intelligence: Current trends”
Md Sarker, Lu Zhou, Aaron Eberhart and Pascal Hitzler · 2021
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“Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion”
Prithviraj Sen et al · 2021
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“Neural-symbolic integration: A compositional perspective”
Efthymia Tsamoura, Timothy Hospedales and Loizos Michael · 2021
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“ConsisRec: Enhancing GNN for social recommendation via consistent neighbor aggregation”
Liangwei Yang et al · 2021
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“Understanding how pretraining regularizes deep learning algorithms”
Yu Yao, Baosheng Yu, Chen Gong and Tongliang Liu · 2021
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“Neural, symbolic and neural-symbolic reasoning on knowledge graphs”
Jing Zhang et al · 2021
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“Drug repurposing for COVID-19 via knowledge graph completion”
Rui Zhang et al · 2021
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“A survey on neural network interpretability”
Yu Zhang, Peter Tiňo, Aleš Leonardis and Ke Tang · 2021
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“Neuro-symbolic entropy regularization”
Kareem Ahmed, Eric Wang, Kai-Wei Chang and Guy Van · 2022
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“Semantic probabilistic layers for neuro-symbolic learning”
Kareem Ahmed et al · 2022
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“Oversquashing in GNNs through the lens of information contraction and graph expansion”
Pradeep Banerjee et al · 2022
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“Combining Embeddings and Rules for Fact Prediction”
Armand Boschin, Nitisha Jain, Gurami Keretchashvili and Fabian Suchanek · 2022
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“Graph Collaborative Reasoning”
Hanxiong Chen et al · 2022
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“QLogicE: Quantum Logic Empowered Embedding for Knowledge Graph Completion”
Panfeng Chen, Yisong Wang, Xiaomin Yu and Renyan Feng · 2022
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“Palm: Scaling language modeling with pathways”
Aakanksha Chowdhery et al · 2022
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“Neuro-symbolic approaches in Artificial Intelligence”
Pascal Hitzler et al · 2022
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“Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning”
Zoi Kaoudi, Abelardo Lorenzo and Volker Markl · 2022
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“The third AI summer: AAAI Robert S. Engelmore Memorial Lecture”
Henry. Kautz · 2022
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“Interpretable Machine Learning”, 2022
Christoph Molnar · 2022
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“Patient Rights And Ethics”
Jacob Olejarczyk and Michael Young · 2022
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“Ensembles of knowledge graph embedding models improve predictions for drug discovery”
Daniel Rivas-Barragan, Daniel Domingo-Fernández, Yojana Gadiya and David Healey · 2022
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“The Human Disease Ontology 2022 update”
Lynn Schriml et al · 2022
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“Heterogeneous Graph Neural Network With Multi-View Representation Learning”
Zezhi Shao et al · 2022
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“Knowledge-aware recommendations based on neuro-symbolic graph embeddings and first-order logical rules”
Giuseppe Spillo et al · 2022
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“Multimodal deep learning for biomedical data fusion: a review”
Sören Stahlschmidt, Benjamin Ulfenborg and Jane Synnergren · 2022
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“GUIDE: Training Deep Graph Neural Networks via Guided Dropout Over Edges”
Jie Wang, Jianqing Liang, Jiye Liang and Kaixuan Yao · 2022
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“Traversenet: Unifying space and time in message passing for traffic forecasting”
Zonghan Wu et al · 2022
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“Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks”
Yujun Yan et al · 2022
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“Graph few-shot learning via restructuring task graph”
Feng Zhao, Tiancheng Huang and Donglin Wang · 2022
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“Adapting Neural Link Predictors for Data-Efficient Complex Query Answering”
Erik Arakelyan et al · 2023
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“Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions”
Chengzhi Cao, Chao Yang, Ruimao Zhang and Shuang Li · 2023
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“GCNH: A Simple Method For Representation Learning On Heterophilous Graphs”
Andrea Cavallo et al · 2023
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“Knowledge graph-enhanced molecular contrastive learning with functional prompt”
Yin Fang et al · 2023
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“Neurosymbolic AI: The 3 rd wave”
Artur’Avila Garcez and Luis Lamb · 2023
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Aryo Gema et al · 2023
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“On the Benefits of OWL-based Knowledge Graphs for Neural-Symbolic Systems”
David Herron, Ernesto Jiménez-Ruiz and Tillman Weyde · 2023
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“ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction”
Monika Jain, Kuldeep Singh and Raghava Mutharaju · 2023
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“Utilizing Perturbation of Atoms’ Positions for Equivariant Pre-Training in 3D Molecular Analysis”
Tal Kiani et al · 2023
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“MultiGML: multimodal graph machine learning for prediction of adverse drug events”
Sophia Krix et al · 2023
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“Few-Shot Relation Extraction With Dual Graph Neural Network Interaction”
Jing Li, Shanshan Feng and Billy Chiu · 2023
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“How to Turn Your Knowledge Graph Embeddings into Generative Models”
Lorenzo Loconte, Nicola Di, Robert Peharz and Antonio Vergari · 2023
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“Determining the Optimal Number of GAT and GCN Layers for Node Classification in Graph Neural Networks”
Humaira Noor et al · 2023
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“Empowering simple graph convolutional networks”
Luca Pasa, Nicolo Navarin, Wolfgang Erb and Alessandro Sperduti · 2023
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“A survey on oversmoothing in graph neural networks”
T Rusch, Michael Bronstein and Siddhartha Mishra · 2023
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“Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs”
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“A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects”, 2023
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“Knowledge Enhanced Graph Neural Networks for Graph Completion”
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“A Critical Review of Inductive Logic Programming Techniques for Explainable AI”
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“How does over-squashing affect the power of GNNs?”
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