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Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources.
Docgraphlm: Documental graph language model for information extraction
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The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider · 2004
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Simple bm25 extension to multiple weighted fields
Stephen Robertson, Hugo Zaragoza, and Michael Taylor · 2004
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Heterogeneous graph neural networks for extractive document summarization
Danqing Wang, Pengfei Liu, Yining Zheng, Xipeng Qiu, and Xuanjing Huang · 2004
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Every document owns its structure: Inductive text classification via graph neural networks
Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, and Liang Wang · 2004
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Random geometric graphs, 2005
Chris Cannings · 2005
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Zinc- a free database of commercially available compounds for virtual screening
John J Irwin and Brian K Shoichet · 2005
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Extracting summary sentences based on the document semantic graph
Jure Leskovec, Natasa Milic-Frayling, and Marko Grobelnik · 2005
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Leveraging graph to improve abstractive multi-document summarization
Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, and Junping Du · 2005
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Graph-based text classification: learn from your neighbors
Ralitsa Angelova and Gerhard Weikum · 2006
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Relational database: A practical foundation for productivity
Edgar F Codd · 2007
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
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Image retrieval with graph kernel on regions
Justine Lebrun, Sylvie Philipp-Foliguet, and Philippe-Henri Gosselin · 2008
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al · 2009
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Global-to-local neural networks for document-level relation extraction
Difeng Wang, Wei Hu, Ermei Cao, and Weijian Sun · 2009
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Tagme: on-the-fly annotation of short text fragments (by wikipedia entities)
Paolo Ferragina and Ugo Scaiella · 2010
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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A graph representation of semi-structured data for web question answering
Xingyao Zhang, Linjun Shou, Jian Pei, Ming Gong, Lijie Wen, and Daxin Jiang · 2010
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Matpower: Steady-state operations, planning, and analysis tools for power systems research and education
Ray Daniel Zimmerman, Carlos Edmundo Murillo-Sánchez, and Robert John Thomas · 2010
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Qianglong Chen, Feng Ji, Haiqing Chen, and Yin Zhang · 2011
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Random graphs
Svante Janson, Tomasz Luczak, and Andrzej Rucinski · 2011
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Stanford’s multi-pass sieve coreference resolution system at the conll-2011 shared task
Heeyoung Lee, Yves Peirsman, Angel Chang, Nathanael Chambers, Mihai Surdeanu, and Dan Jurafsky · 2011
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Pubmed and beyond: a survey of web tools for searching biomedical literature
Zhiyong Lu · 2011
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Graph-based natural language processing and information retrieval
R Mihalcea · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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The clinicaltrials. gov results database—update and key issues
Deborah A Zarin, Tony Tse, Rebecca J Williams, Robert M Califf, and Nicholas C Ide · 2011
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Chembl: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al · 2012
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Document-topic hierarchies from document graphs
Tim Weninger, Yonatan Bisk, and Jiawei Han · 2012
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Abstract meaning representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider · 2013
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Transposition of native chromatin for multimodal regulatory analysis and personal epigenomics
Jason D Buenrostro, Paul G Giresi, Lisa C Zaba, Howard Y Chang, and William J Greenleaf · 2013
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Event-centered information retrieval using kernels on event graphs
Goran Glavaš and Jan Šnajder · 2013
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
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Introducing letor 4.0 datasets
Tao Qin and Tie-Yan Liu · 2013
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Social influence locality for modeling retweeting behaviors
Jing Zhang, Biao Liu, Jie Tang, Ting Chen, and Juanzi Li · 2013
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A graph based analysis of leak localization in urban water networks
Antonio Candelieri, Dante Conti, and Francesco Archetti · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Knowledge-based graph document modeling
Michael Schuhmacher and Simone Paolo Ponzetto · 2014
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Graph based representation and analysis of text document: A survey of techniques
Sheetal S Sonawane and Parag A Kulkarni · 2014
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 2014
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Deep feature synthesis: Towards automating data science endeavors
James Max Kanter and Kalyan Veeramachaneni · 2015
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The technology and biology of single-cell rna sequencing
Aleksandra A Kolodziejczyk, Jong Kyoung Kim, Valentine Svensson, John C Marioni, and Sarah A Teichmann · 2015
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Pos tagging approaches: A comparison
Deepika Kumawat and Vinesh Jain · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel · 2015
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Retrieval of relevant opinion sentences for new products
Dae Hoon Park, Hyun Duk Kim, ChengXiang Zhai, and Lifan Guo · 2015
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The network data repository with interactive graph analytics and visualization
Ryan Rossi and Nesreen Ahmed · 2015
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An overview of the bioasq large-scale biomedical semantic indexing and question answering competition
George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, et al · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Gksh: Graph based image retrieval using supervised kernel hashing
Bo Wu, Bo Lang, and Yang Liu · 2016
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On edge classification in networks with structure and content
Charu C Aggarwal, Yao Li, S Yu Philip, and Yuchen Zhao · 2017
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Text summarization using abstract meaning representation
Shibhansh Dohare, Harish Karnick, and Vivek Gupta · 2017
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Learning to generate product reviews from attributes
Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu · 2017
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Uci machine learning repository
Dheeru Dua, Casey Graff, et al · 2017
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An algorithm to identify functional groups in organic molecules
Peter Ertl · 2017
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Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani · 2017
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Zero-shot transfer learning for event extraction
Lifu Huang, Heng Ji, Kyunghyun Cho, and Clare R Voss · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Sachin Pawar, Girish K Palshikar, and Pushpak Bhattacharyya · 2017
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Social network de-anonymization and privacy inference with knowledge graph model
Jianwei Qian, Xiang-Yang Li, Chunhong Zhang, Linlin Chen, Taeho Jung, and Junze Han · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Graph-based neural multi-document summarization
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, and Dragomir Radev · 2017
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Learning role-based graph embeddings
Nesreen K Ahmed, Ryan Rossi, John Boaz Lee, Theodore L Willke, Rong Zhou, Xiangnan Kong, and Hoda Eldardiry · 2018
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Knowedu: A system to construct knowledge graph for education
Penghe Chen, Yu Lu, Vincent W Zheng, Xiyang Chen, and Boda Yang · 2018
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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
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Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2018
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Contextual stochastic block models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, and Elchanan Mossel · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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Neural models for reasoning over multiple mentions using coreference
Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2018
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Learning structural node embeddings via diffusion wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec · 2018
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Wikihow: A large scale text summarization dataset
Mahnaz Koupaee and William Yang Wang · 2018
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Matching long text documents via graph convolutional networks
Bang Liu, Ting Zhang, Di Niu, Jinghong Lin, Kunfeng Lai, and Yu Xu · 2018
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Networks
Mark Newman · 2018
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Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer’s disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrero, Ben Glocker, and Daniel Rueckert · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Cyclegen: Cyclic consistency based product review generator from attributes
Vasu Sharma, Harsh Vardhan Sharma, Ankita Bishnu, and Labhesh Patel · 2018
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Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William Cohen · 2018
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Graph2seq: Graph to sequence learning with attention-based neural networks
Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, and Vadim Sheinin · 2018
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An integrated graph model for document summarization
Kang Yang, Kamal Al-Sabahi, Yanmin Xiang, and Zuping Zhang · 2018
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A graph based document retrieval method
Zhiqiang Zhang, Linan Wang, Xiaoqin Xie, and Haiwei Pan · 2018
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Learning to retrieve reasoning paths over wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong · 2019
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Query expansion techniques for information retrieval: a survey
Hiteshwar Kumar Azad and Akshay Deepak · 2019
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Preliminary study on the construction of chinese medical knowledge graph
Odma Byambasuren, Yunfei Yang, Zhifang Sui, Damai Dai, Baobao Chang, Sujian Li, and Hongying Zan · 2019
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Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou · 2019
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Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu · 2019
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Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Pubchem 2019 update: improved access to chemical data
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al · 2019
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Text generation from knowledge graphs with graph transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi · 2019
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Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction
Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang · 2019
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Hyperbolic graph neural networks
Qi Liu, Maximilian Nickel, and Douwe Kiela · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu · 2019
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Multiplexed detection of proteins, transcriptomes, clonotypes and crispr perturbations in single cells
Eleni P Mimitou, Anthony Cheng, Antonino Montalbano, Stephanie Hao, Marlon Stoeckius, Mateusz Legut, Timothy Roush, Alberto Herrera, Efthymia Papalexi, Zhengqing Ouyang, et al · 2019
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Knowledge guided text retrieval and reading for open domain question answering
Sewon Min, Danqi Chen, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Dynamically fused graph network for multi-hop reasoning
Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu · 2019
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Inter-sentence relation extraction with document-level graph convolutional neural network
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou · 2019
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Simple bert models for relation extraction and semantic role labeling
Peng Shi and Jimmy Lin · 2019
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Integrative single-cell analysis
Tim Stuart and Rahul Satija · 2019
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Pullnet: Open domain question answering with iterative retrieval on knowledge bases and text
Haitian Sun, Tania Bedrax-Weiss, and William Cohen · 2019
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STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
Damian Szklarczyk, Annika L Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, Nadezhda T Doncheva, John H Morris, Peer Bork, Lars J Jensen, and Christian von Mering · 2019
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Identifying supporting facts for multi-hop question answering with document graph networks
Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel, and André Freitas · 2019
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2019
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Aspect-based sentiment classification with aspect-specific graph convolutional networks
Chen Zhang, Qiuchi Li, and Dawei Song · 2019
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Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training
Oshin Agarwal, Heming Ge, Siamak Shakeri, and Rami Al-Rfou · 2020
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Named entity extraction for knowledge graphs: A literature overview
Tareq Al-Moslmi, Marc Gallofré Ocaña, Andreas L Opdahl, and Csaba Veres · 2020
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Latent-graph learning for disease prediction
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael Bronstein · 2020
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Supervised learning on relational databases with graph neural networks
Milan Cvitkovic · 2020
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Autogluon-tabular: Robust and accurate automl for structured data
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
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Scalable multi-hop relational reasoning for knowledge-aware question answering
Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, and Xiang Ren · 2020
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A survey on knowledge graph-based recommender systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He · 2020
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Graph representation learning
William L Hamilton · 2020
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou · 2020
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scgnn: scrna-seq dropout imputation via induced hierarchical cell similarity graph
Kexin Huang · 2020
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Traffic classification based on graph convolutional network
Xingguo Ji and Qingmin Meng · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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Retrieval-augmented controllable review generation
Jihyeok Kim, Seungtaek Choi, Reinald Kim Amplayo, and Seung-won Hwang · 2020
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K-bert: Enabling language representation with knowledge graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang · 2020
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Tabprompt: Graph-based pre-training and prompting for few-shot table understanding
Rihui Jin, Jianan Wang, Wei Tan, Yongrui Chen, Guilin Qi, and Wang Hao · 2023
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Kg-gpt: A general framework for reasoning on knowledge graphs using large language models
Jiho Kim, Yeonsu Kwon, Yohan Jo, and Edward Choi · 2023
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Adapting foundation models for operator data analytics
Manikanta Kotaru · 2023
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Conformal prediction with large language models for multi-choice question answering, 2023
Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, and Andrew Beam · 2023
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Tabgsl: Graph structure learning for tabular data prediction
Jay Chiehen Liao and Cheng-Te Li · 2023
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Guoshun Nan, Zhijiang Guo, Ivan Sekulić, and Wei Lu · 2020
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Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Scott Yih · 2020
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Optimal power flow using graph neural networks
Damian Owerko, Fernando Gama, and Alejandro Ribeiro · 2020
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Semantic graphs for generating deep questions
Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, and Min-Yen Kan · 2020
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Exploiting cross-session information for session-based recommendation with graph neural networks
Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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xfraud: explainable fraud transaction detection
Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, and Ce Zhang · 2020
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Llm-rec: Personalized recommendation via prompting large language models
Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Christopher Leung, Jiajie Tang, and Jiebo Luo · 2023
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Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos, Caiming Xiong, Zachary Z Sun, Richard Socher, et al · 2023
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Atlantic: Structure-aware retrieval-augmented language model for interdisciplinary science
Sai Munikoti, Anurag Acharya, Sridevi Wagle, and Sameera Horawalavithana · 2023
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Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study
Lino Murali, G Gopakumar, Daleesha M Viswanathan, and Prema Nedungadi · 2023
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A retrieve-and-read framework for knowledge graph link prediction
Vardaan Pahuja, Boshi Wang, Hugo Latapie, Jayanth Srinivasa, and Yu Su · 2023
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Graph-guided reasoning for multi-hop question answering in large language models
Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J Kim, and Joo-Kyung Kim · 2023
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Single sequence prediction over reasoning graphs for multi-hop qa
Gowtham Ramesh, Makesh Sreedhar, and Junjie Hu · 2023
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Lamp: When large language models meet personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani · 2023
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Taskbench: Benchmarking large language models for task automation
Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, and Yueting Zhuang · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou · 2023
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Graphfc: Customs fraud detection with label scarcity
Karandeep Singh, Yu-Che Tsai, Cheng-Te Li, Meeyoung Cha, and Shou-De Lin · 2023
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Tabular representation, noisy operators, and impacts on table structure understanding tasks in llms
Ananya Singha, José Cambronero, Sumit Gulwani, Vu Le, and Chris Parnin · 2023
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Single-cell multimodal prediction via transformers
Wenzhuo Tang, Hongzhi Wen, Renming Liu, Jiayuan Ding, Wei Jin, Yuying Xie, Hui Liu, and Jiliang Tang · 2023
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Grapeqa: Graph augmentation and pruning to enhance question-answering
Dhaval Taunk, Lakshya Khanna, Siri Venkata Pavan Kumar Kandru, Vasudeva Varma, Charu Sharma, and Makarand Tapaswi · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Large language models in medicine
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting · 2023
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Maung Thway, Jose Recatala-Gomez, Fun Siong Lim, Kedar Hippalgaonkar, and Leonard W. T. Ng · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Sridevi Wagle, Sai Munikoti, Anurag Acharya, Sara Smith, and Sameera Horawalavithana · 2023
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Zero-shot next-item recommendation using large pretrained language models
Lei Wang and Ee-Peng Lim · 2023
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