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In contrast to large text corpora, knowledge graphs (KG) provide dense and structured representations of factual information.
A note on two problems in connexion with graphs
Edsger Wybe Dijkstra. 1959 · 1959
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The rectilinear steiner tree problem is np-complete
Michael R Garey and David S. Johnson. 1977 · 1977
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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 · 2008
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
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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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BERT: Pre-training of deep bidirectional transformers for language understanding
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Story ending generation with incremental encoding and commonsense knowledge
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When choosing plausible alternatives, clever hans can be clever
Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, and Kentaro Inui. 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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Knowledge enhanced contextual word representations
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Language models are unsupervised multitask learners
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Incorporating commonsense knowledge graph in pretrained models for social commonsense tasks
Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, and Dilek Hakkani-Tur. 2020 · 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 · 2020
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Common sense or world knowledge? investigating adapter-based knowledge injection into pretrained transformers
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Connecting the dots: A knowledgeable path generator for commonsense question answering
Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, and Xiang Ren. 2020 · 2020
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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. 2021 · 2021
Revisiting few-sample BERT fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2021 · 2021
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Identifying relevant common sense information in knowledge graphs
Guy Aglionby and Simone Teufel. 2022 · 2022
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COPA-SSE: Semi-structured explanations for commonsense reasoning
Ana Brassard, Benjamin Heinzerling, Pride Kavumba, and Kentaro Inui. 2022 · 2022
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ComFact: A benchmark for linking contextual commonsense knowledge
Silin Gao, Jena D. Hwang, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, and Antoine Bosselut. 2022 · 2022
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Learning from missing relations: Contrastive learning with commonsense knowledge graphs for commonsense inference
Yong-Ho Jung, Jun-Hyung Park, Joon-Young Choi, Mingyu Lee, Junho Kim, Kang-Min Kim, and SangKeun Lee. 2022 · 2022
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Identify, align, and integrate: Matching knowledge graphs to commonsense reasoning tasks
Lisa Bauer and Mohit Bansal. 2021 · 2021
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COCO-EX: A tool for linking concepts from texts to ConceptNet
Maria Becker, Katharina Korfhage, and Anette Frank. 2021 · 2021
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Incorporating commonsense knowledge into abstractive dialogue summarization via heterogeneous graph networks
Xiachong Feng, Xiaocheng Feng, and Bing Qin. 2021 · 2021
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Improving unsupervised commonsense reasoning using knowledge-enabled natural language inference
Canming Huang, Weinan He, and Yongmei Liu. 2021 · 2021
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ExplaGraphs: An explanation graph generation task for structured commonsense reasoning
Swarnadeep Saha, Prateek Yadav, Lisa Bauer, and Mohit Bansal. 2021 · 2021
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QA-GNN: Reasoning with language models and knowledge graphs for question answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. 2021 · 2021
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Jivat Kaur, Sumit Bhatia, Milan Aggarwal, Rachit Bansal, and Balaji Krishnamurthy. 2022 · 2022
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TIARA: Multi-grained retrieval for robust question answering over large knowledge base
Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje Karlsson, Tingting Ma, Yuzhong Qu, and Chin-Yew Lin. 2022 · 2022
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JointLK: Joint reasoning with language models and knowledge graphs for commonsense question answering
Yueqing Sun, Qi Shi, Le Qi, and Yu Zhang. 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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KG-FiD: Infusing knowledge graph in fusion-in-decoder for open-domain question answering
Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, and Michael Zeng. 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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