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While Language Models (LMs) are the workhorses of NLP, their interplay with structured knowledge graphs (KGs) is still actively researched.
Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007 · 2007
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Wikidata: A free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
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The WebNLG challenge: Generating text from RDF data
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
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A convolutional encoder model for neural machine translation
Jonas Gehring, Michael Auli, David Grangier, and Yann Dauphin. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018 · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 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 · 2019
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KagNet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren. 2019 · 2019
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2020 · 2020
Earlier work this paper cites.
Comet-atomic 2020: On symbolic and neural commonsense knowledge graphs
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2021 · 2020
Earlier work this paper cites.
Commonsense knowledge base completion with structural and semantic context
Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
Modeling global and local node contexts for text generation from knowledge graphs
Leonardo F. R. Ribeiro, Yue Zhang, Claire Gardent, and Iryna Gurevych. 2020 · 2020
Cited alongside, same era.
An unsupervised joint system for text generation from knowledge graphs and semantic parsing
Martin Schmitt, Sahand Sharifzadeh, Volker Tresp, and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Connecting the dots: A knowledgeable path generator for commonsense question answering
Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, and Xiang Ren. 2020a · 2020
Cited alongside, same era.
Position information in transformers: An overview
Philipp Dufter, Martin Schmitt, and Hinrich Schütze. 2022 · 2022
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Transformer for graphs: An overview from architecture perspective
Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wen bing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, and Yu Rong. 2022 · 2022
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah Smith, and Mike Lewis. 2022 · 2022
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Deep bidirectional language-knowledge graph pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy Liang, and Jure Leskovec. 2022 · 2022
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GreaseLM: Graph REASoning enhanced language models
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D Manning, and Jure Leskovec. 2022 · 2022
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav. 2021 · 2021
Cited alongside, same era.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. 2021 · 2021
Cited alongside, same era.
REBEL: Relation extraction by end-to-end language generation
Pere-Lluís Huguet Cabot and Roberto Navigli. 2021 · 2021
Cited alongside, same era.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
Junyi Li, Tianyi Tang, Wayne Xin Zhao, Zhicheng Wei, Nicholas Jing Yuan, and Ji-Rong Wen. 2021 · 2021
Cited alongside, same era.
Investigating pretrained language models for graph-to-text generation
Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, and Iryna Gurevych. 2021 · 2021
Cited alongside, same era.
Modeling graph structure via relative position for text generation from knowledge graphs
Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych, and Hinrich Schütze. 2021 · 2021
Cited alongside, same era.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu. 2021 · 2021
Cited alongside, same era.
Attending to Graph Transformers
Luis Müller, Christopher Morris, Mikhail Galkin, and Ladislav Rampášek. 2023 · 2023
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Similarity-weighted construction of contextualized commonsense knowledge graphs for knowledge-intense argumentation tasks
Moritz Plenz, Juri Opitz, Philipp Heinisch, Philipp Cimiano, and Anette Frank. 2023 · 2023
Later among the works it cites.
NovaCOMET: Open commonsense foundation models with symbolic knowledge distillation
Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, and Yejin Choi. 2023 · 2023
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Learning on large-scale text-attributed graphs via variational inference
Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. 2023 · 2023
Later among the works it cites.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024 · 2024
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Unifying Structured Data as Graph for Data-to-Text Pre-Training
Shujie Li, Liang Li, Ruiying Geng, Min Yang, Binhua Li, Guanghu Yuan, Wanwei He, Shao Yuan, Can Ma, Fei Huang, and Yongbin Li. 2024 · 2024
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Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024 · 2024
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Pakt: Perspectivized argumentation knowledge graph and tool for deliberation analysis
Moritz Plenz, Philipp Heinisch, Anette Frank, and Philipp Cimiano. 2024 · 2024
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