{"type":"ScholarlyArticle","title":"Contrastive Representation Learning for Self-Supervised Taxonomy Completion","authors":[{"name":"Yuhang Niu","slug":"yuhang-niu","given":"Yuhang","family":"Niu","affiliation":"College of Computer Science , Nankai University , Tianjin , China"},{"name":"Hongyuan Xu","slug":"hongyuan-xu","given":"Hongyuan","family":"Xu","affiliation":"College of Computer Science , Nankai University , Tianjin , China"},{"name":"Ciyi Liu","slug":"ciyi-liu","given":"Ciyi","family":"Liu","affiliation":"College of Computer Science , Nankai University , Tianjin , China"},{"name":"Yanlong Wen","slug":"yanlong-wen","given":"Yanlong","family":"Wen","affiliation":"College of Computer Science , Nankai University , Tianjin , China"},{"name":"Xiaojie Yuan","slug":"xiaojie-yuan","given":"Xiaojie","family":"Yuan","affiliation":"College of Computer Science , Nankai University , Tianjin , China"}],"abstract":"Taxonomy completion, a self-supervised task, aims to add new concepts to an existing taxonomy by attaching them to appropriate hypernym and hyponym pairs. Researchers have proposed several approaches to capture the essential relationships in taxonomy using semantic or structural information. However, they either construct training signals from a single view or simply use a random sampling strategy, making it insufficient to capture various relations in taxonomic structure and learn quality representations. To address this, we propose CoSTC, a contrastive learning framework that captures diverse relations and improves representations for taxonomy completion. It uses two contrasting views, namely intra-view and inter-view, to provide rich self-supervised signals. In intra-view contrasting, we exploit the correlations within queries and within positions by performing instance-level discrimination task. In inter-view contrasting, we use a sampling strategy that considers diversity and hardness to select representative pairs, enhancing the learning of fine-grained query-position relations. Experimental results on three datasets verify the effectiveness of our approach. Our code is available at https://github.com/nyh-a/CoSTC.","doi":"10.24963/ijcai.2024/712","url":"https://ijcai.org/proceedings/2024/712","pdf":"https://ijcai.org/proceedings/2024/0712.pdf","markdown":"https://ijcai.org/proceedings/2024/712.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/712","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":712,"pages":{"first":6442,"last":6450},"keywords":["Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[{"position":1,"raw":"[Arous et al., 2023] Ines Arous, Ljiljana Dolamic, and Philippe Cudré-Mauroux. Taxocomplete: Self-supervised taxonomy completion leveraging position-enhanced se- mantic matching. In WWW, pages 2509-2518, 2023.","title":"Taxocomplete: Self-supervised taxonomy completion leveraging position-enhanced semantic matching","authors":"Ines Arous, Ljiljana Dolamic, Philippe Cudré-Mauroux","year":"2023","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bordea et al., 2015] Georgeta Bordea, Paul Buitelaar, Ste- fano Faralli, and Roberto Navigli. Semeval-2015 task 17: Taxonomy extraction evaluation (texeval). In SemEval@NAACL-HLT 2015, pages 902-910, 2015.","title":"Semeval-2015 task 17: Taxonomy extraction evaluation (texeval)","authors":"Georgeta Bordea, Paul Buitelaar, Stefano Faralli, Roberto Navigli","year":"2015","venue":"SemEval@NAACL-HLT 2015","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey E. Hinton. A simple frame- work for contrastive learning of visual representations. In ICML, volume 119 of PMLR, pages 1597-1607, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E Hinton","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2022] Yiming Chen, Yan Zhang, Bin Wang, Zuozhu Liu, and Haizhou Li. Generate, discriminate and contrast: A semi-supervised sentence representation learn- ing framework. In EMNLP, pages 8150-8161, 2022.","title":"Generate, discriminate and contrast: A semi-supervised sentence representation learning framework","authors":"Yiming Chen, Yan Zhang, Bin Wang, Zuozhu Liu, Haizhou Li","year":"2022","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2022] Sijie Cheng, Zhouhong Gu, Bang Liu, Rui Xie, Wei Wu, and Yanghua Xiao. Learning what you need from what you did: Product taxonomy expansion with user behaviors supervision. In ICDE, pages 3280- 3293, 2022.","title":"Learning what you need from what you did: Product taxonomy expansion with user behaviors supervision","authors":"Sijie Cheng, Zhouhong Gu, Bang Liu, Rui Xie, Wei Wu, Yanghua Xiao","year":"2022","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dahiya et al., 2023] Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, et al. Ngame: Neg- ative mining-aware mini-batching for extreme classifica- tion. In WSDM, pages 258-266, 2023.","title":"Ngame: Negative mining-aware mini-batching for extreme classification","authors":"Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, K Gururaj, Prasenjit Dey, Amit Singh","year":"2023","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In NAACL, pages 4171-4186, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gao et al., 2021] Tianyu Gao, Xingcheng Yao, and Danqi Chen. Simcse: Simple contrastive learning of sentence embeddings. In EMNLP, pages 6894-6910, 2021.","title":"Simcse: Simple contrastive learning of sentence embeddings","authors":"Tianyu Gao, Xingcheng Yao, Danqi Chen","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ge et al., 2023] Songwei Ge, Shlok Mishra, Simon Korn- blith, Chun-Liang Li, and David Jacobs. Hyperbolic con- trastive learning for visual representations beyond objects. In CVPR 2023, pages 6840-6849, 2023.","title":"Hyperbolic contrastive learning for visual representations beyond objects","authors":"Ge","year":"2023","venue":"CVPR 2023","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gutmann and Hyvärinen, 2010] Michael Gutmann and Aapo Hyvärinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS, volume 9 of JMLR, pages 297-304, 2010.","title":"Noise-contrastive estimation: A new estimation principle for unnormalized statistical models","authors":"Hyvärinen Gutmann, Michael Gutmann, Aapo Hyvärinen","year":"2010","venue":"AISTATS, volume 9 of JMLR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hadsell et al., 2006] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In CVPR, pages 1735-1742, 2006.","title":"Dimensionality reduction by learning an invariant mapping","authors":"Raia Hadsell, Sumit Chopra, Yann Lecun","year":"2006","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Harwood et al., 2017] Ben Harwood, Vijay Kumar B. G, Gustavo Carneiro, Ian D. Reid, and Tom Drummond. Smart mining for deep metric learning. In ICCV, pages 2840-2848, 2017.","title":"Smart mining for deep metric learning","authors":"Ben Harwood, Vijay Kumar, B G, Gustavo Carneiro, Ian D Reid, Tom Drummond","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[He et al., 2020] Kaiming He, Haoqi Fan, Yuxin Wu, Sain- ing Xie, and Ross B. Girshick. Momentum contrast for un- supervised visual representation learning. In CVPR, pages 9726-9735, 2020.","title":"Momentum contrast for unsupervised visual representation learning","authors":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross B Girshick","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jiang et al., 2022a] Minhao Jiang, Xiangchen Song, Jieyu Zhang, and Jiawei Han. Taxoenrich: Self-supervised tax- onomy completion via structure-semantic representations. In WWW, pages 925-934, 2022.","title":"Taxoenrich: Self-supervised taxonomy completion via structure-semantic representations","authors":"Jiang","year":"2022","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jiang et al., 2022b] Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, and Qi Zhang. Promptbert: Improving BERT sentence embeddings with prompts. In EMNLP, pages 8826-8837, 2022.","title":"Promptbert: Improving BERT sentence embeddings with prompts","authors":"Jiang","year":"2022","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jiang et al., 2023] Song Jiang, Qiyue Yao, Qifan Wang, and Yizhou Sun. A single vector is not enough: Taxonomy expansion via box embeddings. In WWW, pages 2467- 2476, 2023.","title":"A single vector is not enough: Taxonomy expansion via box embeddings","authors":"Song Jiang, Qiyue Yao, Qifan Wang, Yizhou Sun","year":"2023","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jurgens and Pilehvar, 2016] David Jurgens and Moham- mad Taher Pilehvar. Semeval-2016 task 14: Semantic taxonomy enrichment. In SemEval@NAACL-HLT 2016, pages 1092-1102, 2016.","title":"Semeval-2016 task 14: Semantic taxonomy enrichment","authors":"David Jurgens, Mohammad Taher, Pilehvar","year":"2016","venue":"SemEval@NAACL-HLT 2016","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Karamanolakis et al., 2020] Giannis Karamanolakis, Jun Ma, and Xin Luna Dong. Txtract: Taxonomy-aware knowledge extraction for thousands of product categories. In ACL, pages 8489-8502, 2020.","title":"Txtract: Taxonomy-aware knowledge extraction for thousands of product categories","authors":"Giannis Karamanolakis, Jun Ma, Xin Luna, Dong","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2023] Wen-Zhi Li, Chang-Dong Wang, Hui Xiong, and Jian-Huang Lai. Homogcl: Rethinking ho- mophily in graph contrastive learning. In SIGKDD, pages 1341-1352, 2023.","title":"Homogcl: Rethinking homophily in graph contrastive learning","authors":"Wen-Zhi Li, Chang-Dong Wang, Hui Xiong, Jian-Huang Lai","year":"2023","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lipscomb, 2000] Carolyn E Lipscomb. Medical subject headings (mesh). BMLA, page 265, 2000.","title":"Medical subject headings (mesh)","authors":"Carolyn E Lipscomb","year":"2000","venue":"BMLA","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2017] Jingzhou Liu, Wei-Cheng Chang, Yuexin Wu, and Yiming Yang. Deep learning for extreme multi- label text classification. In SIGIR, pages 115-124. ACM, 2017.","title":"Deep learning for extreme multilabel text classification","authors":"Jingzhou Liu, Wei-Cheng Chang, Yuexin Wu, Yiming Yang","year":"2017","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2021] Zichen Liu, Hongyuan Xu, Yanlong Wen, Ning Jiang, Haiying Wu, and Xiaojie Yuan. TEMP: taxonomy expansion with dynamic margin loss through taxonomy-paths. In EMNLP, pages 3854-3863, 2021.","title":"TEMP: taxonomy expansion with dynamic margin loss through taxonomy-paths","authors":"Liu","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2023] Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, and Jin Song Dong. B 2 -sampling: Fusing balanced and biased sampling for graph contrastive learning. In SIGKDD, pages 1489-1500, 2023.","title":"B 2 -sampling: Fusing balanced and biased sampling for graph contrastive learning","authors":"Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, Jin Song, Dong","year":"2023","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Loshchilov and Hutter, 2018] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2018.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Luo et al., 2022] Yifei Luo, Minghui Xu, and Deyi Xiong. Cogtaskonomy: Cognitively inspired task taxonomy is beneficial to transfer learning in NLP. In ACL, pages 904- 920, 2022.","title":"Cogtaskonomy: Cognitively inspired task taxonomy is beneficial to transfer learning in NLP","authors":"Yifei Luo, Minghui Xu, Deyi Xiong","year":"2022","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Manzoor et al., 2020] Emaad Manzoor, Rui Li, Dhananjay Shrouty, and Jure Leskovec. Expanding taxonomies with implicit edge semantics. In WWW, pages 2044-2054, 2020.","title":"Expanding taxonomies with implicit edge semantics","authors":"Manzoor","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Nie et al., 2023] Zhijie Nie, Richong Zhang, and Yongyi Mao. On the inadequacy of optimizing alignment and uni- formity in contrastive learning of sentence representations. In ICLR, 2023.","title":"On the inadequacy of optimizing alignment and uniformity in contrastive learning of sentence representations","authors":"Zhijie Nie, Richong Zhang, Yongyi Mao","year":"2023","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Phukon et al., 2022] Bornali Phukon, Anasua Mitra, Sanasam Ranbir Singh, and Priyankoo Sarmah. TEAM: A multitask learning based taxonomy expansion approach for attach and merge. In NAACL Findings, pages 366-378, 2022.","title":"Bornali Phukon, Anasua Mitra, Sanasam Ranbir Singh, and Priyankoo Sarmah. TEAM: A multitask learning based taxonomy expansion approach for attach and merge","authors":"Phukon","year":"2022","venue":"NAACL Findings","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Robinson et al., 2021] Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. Contrastive learning with hard negative samples. In ICLR, 2021.","title":"Contrastive learning with hard negative samples","authors":"Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, Stefanie Jegelka","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Schroff et al., 2015] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In CVPR, pages 815-823, 2015.","title":"Facenet: A unified embedding for face recognition and clustering","authors":"Schroff","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Shen et al., 2020] Jiaming Shen, Zhihong Shen, Chenyan Xiong, Chi Wang, Kuansan Wang, and Jiawei Han. Taxoexpan: Self-supervised taxonomy expansion with position-enhanced graph neural network. In WWW, pages 486-497, 2020.","title":"Taxoexpan: Self-supervised taxonomy expansion with position-enhanced graph neural network","authors":"Jiaming Shen, Zhihong Shen, Chenyan Xiong, Chi Wang, Kuansan Wang, Jiawei Han","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sun et al., 2024] Kai Sun, Jifan Yu, Juanzi Li, and Lei Hou. Exploring sequence-to-sequence taxonomy expansion via language model probing. ESWA, 239:122321, 2024.","title":"Exploring sequence-to-sequence taxonomy expansion via language model probing","authors":"Kai Sun, Jifan Yu, Juanzi Li, Lei Hou","year":"2024","venue":"ESWA","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Takeoka et al., 2021] Kunihiro Takeoka, Kosuke Akimoto, and Masafumi Oyamada. Low-resource taxonomy enrich- ment with pretrained language models. In EMNLP, pages 2747-2758, 2021.","title":"Low-resource taxonomy enrichment with pretrained language models","authors":"Kunihiro Takeoka, Kosuke Akimoto, Masafumi Oyamada","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2021a] Suyuchen Wang, Ruihui Zhao, Xi Chen, Yefeng Zheng, and Bang Liu. Enquire one's parent and child before decision: Fully exploit hierarchi- cal structure for self-supervised taxonomy expansion. In WWW, pages 3291-3304, 2021.","title":"Enquire one's parent and child before decision: Fully exploit hierarchical structure for self-supervised taxonomy expansion","authors":"Wang","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2021b] Xuan Wang, Vivian Hu, Xiangchen Song, Shweta Garg, Jinfeng Xiao, and Jiawei Han. Chem- ner: Fine-grained chemistry named entity recognition with ontology-guided distant supervision. In EMNLP, pages 5227-5240, 2021.","title":"Chemner: Fine-grained chemistry named entity recognition with ontology-guided distant supervision","authors":"Wang","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2022] Suyuchen Wang, Ruihui Zhao, Yefeng Zheng, and Bang Liu. Qen: Applicable taxonomy com- pletion via evaluating full taxonomic relations. In WWW, pages 1008-1017, 2022.","title":"Qen: Applicable taxonomy completion via evaluating full taxonomic relations","authors":"Wang","year":"2022","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2017] Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl. Sampling matters in deep embedding learning. In ICCV, pages 2840-2848, 2017.","title":"Sampling matters in deep embedding learning","authors":"Chao-Yuan, R Wu, Alexander J Manmatha, Philipp Smola, Krahenbuhl","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2018] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non- parametric instance discrimination. In CVPR, pages 3733- 3742, 2018.","title":"Unsupervised feature learning via nonparametric instance discrimination","authors":"Zhirong Wu, Yuanjun Xiong, Stella X Yu, Dahua Lin","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wu et al., 2023] Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song, and Kun Gai. Graph contrastive learning with generative ad- versarial network. In SIGKDD, pages 2721-2730, 2023.","title":"Graph contrastive learning with generative adversarial network","authors":"Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song, Kun Gai","year":"2023","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xiong et al., 2021] Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. Approximate nearest neighbor negative contrastive learning for dense text re- trieval. In ICLR, 2021.","title":"Approximate nearest neighbor negative contrastive learning for dense text retrieval","authors":"Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N Bennett, Junaid Ahmed, Arnold Overwijk","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xu et al., 2022] Hongyuan Xu, Yunong Chen, Zichen Liu, Yanlong Wen, and Xiaojie Yuan. Taxoprompt: A prompt- based generation method with taxonomic context for self- supervised taxonomy expansion. In IJCAI, pages 4432- 4438, 2022.","title":"Taxoprompt: A promptbased generation method with taxonomic context for selfsupervised taxonomy expansion","authors":"Hongyuan Xu, Yunong Chen, Zichen Liu, Yanlong Wen, Xiaojie Yuan","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xu et al., 2023a] Hongyuan Xu, Ciyi Liu, Yuhang Niu, Yunong Chen, Xiangrui Cai, Yanlong Wen, and Xiaojie Yuan. Tacoprompt: A collaborative multi-task prompt learning method for self-supervised taxonomy completion. In EMNLP, pages 15804-15817, 2023.","title":"Tacoprompt: A collaborative multi-task prompt learning method for self-supervised taxonomy completion","authors":"Xu","year":"2023","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xu et al., 2023b] Jiahao Xu, Wei Shao, Lihui Chen, and Lemao Liu. Simcse++: Improving contrastive learning for sentence embeddings from two perspectives. In EMNLP, pages 12028-12040, 2023.","title":"Simcse++: Improving contrastive learning for sentence embeddings from two perspectives","authors":"Xu","year":"2023","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yang et al., 2022] Xinyu Yang, Zhenguo Zhang, and Rongyi Cui. Timeclr: A self-supervised contrastive learn- ing framework for univariate time series representation. KBS, 245:108606, 2022.","title":"Timeclr: A self-supervised contrastive learning framework for univariate time series representation","authors":"Xinyu Yang, Zhenguo Zhang, Rongyi Cui","year":"2022","venue":"KBS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Yu et al., 2020] Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, and Chao Zhang. STEAM: self- supervised taxonomy expansion with mini-paths. In SIGKDD, pages 1026-1035, 2020.","title":"STEAM: selfsupervised taxonomy expansion with mini-paths","authors":"Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, Chao Zhang","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zeng et al., 2021] Qingkai Zeng, Jinfeng Lin, Wenhao Yu, Jane Cleland-Huang, and Meng Jiang. Enhancing tax- onomy completion with concept generation via fusing re- lational representations. In SIGKDD, pages 2104-2113, 2021.","title":"Enhancing taxonomy completion with concept generation via fusing relational representations","authors":"Zeng","year":"2021","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhai et al., 2023] Songlin Zhai, Weiqing Wang, Yuan-Fang Li, and Yuan Meng. DNG: taxonomy expansion by explor- ing the intrinsic directed structure on non-gaussian space. In AAAI, pages 6593-6601, 2023.","title":"DNG: taxonomy expansion by exploring the intrinsic directed structure on non-gaussian space","authors":"Songlin Zhai, Weiqing Wang, Yuan-Fang Li, Yuan Meng","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhang et al., 2014] Yuchen Zhang, Amr Ahmed, Vanja Josifovski, and Alexander J. Smola. Taxonomy discov- ery for personalized recommendation. In WSDM, pages 243-252. ACM, 2014.","title":"Taxonomy discovery for personalized recommendation","authors":"Yuchen Zhang, Amr Ahmed, Vanja Josifovski, Alexander J Smola","year":"2014","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhang et al., 2021] Jieyu Zhang, Xiangchen Song, Ying Zeng, Jiaze Chen, Jiaming Shen, Yuning Mao, and Lei Li. Taxonomy completion via triplet matching network. In AAAI, pages 4662-4670, 2021.","title":"Taxonomy completion via triplet matching network","authors":"Zhang","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhu et al., 2023] Tinghui Zhu, Jingping Liu, Jiaqing Liang, Haiyun Jiang, Yanghua Xiao, Zongyu Wang, Rui Xie, and Yunsen Xian. Towards visual taxonomy expansion. In MM, pages 6481-6490, 2023.","title":"Towards visual taxonomy expansion","authors":"Tinghui Zhu, Jingping Liu, Jiaqing Liang, Haiyun Jiang, Yanghua Xiao, Zongyu Wang, Rui Xie, Yunsen Xian","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}