{"type":"ScholarlyArticle","title":"Enhancing Multimodal Knowledge Graph Representation Learning through Triple Contrastive Learning","authors":[{"name":"Yuxing Lu","slug":"yuxing-lu","given":"Yuxing","family":"Lu","affiliation":"Department of Big Data and Biomedical AI , College of Future Technology , Peking University; Tencent AI Lab"},{"name":"Weichen Zhao","slug":"weichen-zhao","given":"Weichen","family":"Zhao","affiliation":"School of Computer Science and Technology , Soochow University"},{"name":"Nan Sun","slug":"nan-sun","given":"Nan","family":"Sun","affiliation":"School of Computer Science and Technology , Huazhong University of Science & Technology"},{"name":"Jinzhuo Wang","slug":"jinzhuo-wang","given":"Jinzhuo","family":"Wang","affiliation":"Department of Big Data and Biomedical AI , College of Future Technology , Peking University"}],"abstract":"Multimodal knowledge graphs incorporate multimodal information rather than pure symbols, which significantly enhance the representation of knowledge graphs and their capacity to understand the world. Despite these advancements, existing multimodal fusion techniques still face significant challenges in representing modalities and fully integrating the diverse attributes of entities, particularly when dealing with more than one modality. To address this issue, this article proposes a Knowledge Graph Multimodal Representation Learning (KG-MRI) method. This method utilizes foundation models to represent different modalities and incorporates a triple contrastive learning model and a dual-phase training strategy to effectively fuse the different modalities with knowledge graph embeddings. We conducted comprehensive comparisons with several different knowledge graph embedding methods to validate the effectiveness of our KG-MRI model. Furthermore validation on a real-world Non-Alcohol Fatty Liver Disease (NAFLD) cohort demonstrated that the vector representations learned through our methodology possess enhanced representational capabilities, showing promise for broader applications in complex multimodal environments.","doi":"10.24963/ijcai.2024/659","url":"https://ijcai.org/proceedings/2024/659","pdf":"https://ijcai.org/proceedings/2024/0659.pdf","markdown":"https://ijcai.org/proceedings/2024/659.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/659","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":"Multidisciplinary Topics and Applications","track":"Main Track","number":659,"pages":{"first":5963,"last":5971},"keywords":["Data Mining: DM: Knowledge graphs and knowledge base completion","Multidisciplinary Topics and Applications: MTA: Life sciences","Machine Learning: ML: Multi-modal learning","Multidisciplinary Topics and Applications: MTA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Achiam et al., 2023] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Alt- man, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.","title":"Gpt-4 technical report","authors":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ahmad et al., 2022] Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsun- dar. Chemberta-2: Towards chemical foundation models. arXiv preprint arXiv:2209.01712, 2022.","title":"Chemberta-2: Towards chemical foundation models","authors":"Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, Bharath Ramsun","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26, 2013.","title":"Translating embeddings for modeling multi-relational data","authors":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko","year":"2013","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Breiman, 2001] Leo Breiman. Random forests. Machine learning, 45:5-32, 2001.","title":"Leo Breiman. Random forests","authors":"Breiman","year":"2001","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cao et al., 2022] Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. Otkge: Multi-modal knowledge graph embeddings via op- timal transport. Advances in Neural Information Process- ing Systems, 35:39090-39102, 2022.","title":"Otkge: Multi-modal knowledge graph embeddings via optimal transport","authors":"Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chao et al., 2020] Linlin Chao, Jianshan He, Taifeng Wang, and Wei Chu. Pairre: Knowledge graph embeddings via paired relation vectors. arXiv preprint arXiv:2011.03798, 2020.","title":"Pairre: Knowledge graph embeddings via paired relation vectors","authors":"Linlin Chao, Jianshan He, Taifeng Wang, Wei Chu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2020a] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In In- ternational conference on machine learning, pages 1597- 1607. PMLR, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Chen","year":"2020","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2020b] Xiaojun Chen, Shengbin Jia, and Yang Xiang. A review: Knowledge reasoning over knowledge graph. Expert systems with applications, 141:112948, 2020.","title":"A review: Knowledge reasoning over knowledge graph","authors":"Chen","year":"2020","venue":"Expert systems with applications","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cui et al., 2024] Haotian Cui, Chloe Wang, Hassaan Maan, Kuan Pang, Fengning Luo, Nan Duan, and Bo Wang. scgpt: toward building a foundation model for single-cell multi-omics using generative ai. Nature Methods, pages 1-11, 2024.","title":"scgpt: toward building a foundation model for single-cell multi-omics using generative ai","authors":"Cui","year":"2024","venue":"Nature Methods","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Dettmers et al., 2018] Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.","title":"Convolutional 2d knowledge graph embeddings","authors":"Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel","year":"2018","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Devlin et al., 2018] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. arXiv preprint arXiv:1810.04805, 2018.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Dong et al., 2014] Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. Knowledge vault: A web-scale approach to proba- bilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 601-610, 2014.","title":"Knowledge vault: A web-scale approach to probabilistic knowledge fusion","authors":"Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, Wei Zhang","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Galkin et al., 2021] Mikhail Galkin, Etienne Denis, Jiapeng Wu, and William L Hamilton. Nodepiece: Compositional and parameter-efficient representations of large knowledge graphs. arXiv preprint arXiv:2106.12144, 2021.","title":"Nodepiece: Compositional and parameter-efficient representations of large knowledge graphs","authors":"Galkin","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Girdhar et al., 2023] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Armand Alwala, Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180- 15190, 2023.","title":"Imagebind: One embedding space to bind them all","authors":"Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Armand Alwala, Joulin, Ishan Misra","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Guo et al., 2020] Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 34(8):3549-3568, 2020.","title":"A survey on knowledge graph-based recommender systems","authors":"Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He","year":"2020","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[He et al., 2015] Shizhu He, Kang Liu, Guoliang Ji, and Jun Zhao. Learning to represent knowledge graphs with gaus- sian embedding. In Proceedings of the 24th ACM interna- tional on conference on information and knowledge man- agement, pages 623-632, 2015.","title":"Learning to represent knowledge graphs with gaussian embedding","authors":"He","year":"2015","venue":"Proceedings of the 24th ACM international on conference on information and knowledge management","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[He et al., 2022] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked au- toencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022.","title":"Masked autoencoders are scalable vision learners","authors":"Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick","year":"2022","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hearst et al., 1998] Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf. Sup- port vector machines. IEEE Intelligent Systems and their applications, 13(4):18-28, 1998.","title":"Support vector machines","authors":"Marti A Hearst, Susan T Dumais, Edgar Osuna, John Platt, Bernhard Scholkopf","year":"1998","venue":"IEEE Intelligent Systems and their applications","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ji et al., 2015] Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. Knowledge graph embedding via dy- namic mapping matrix. In Proceedings of the 53rd an- nual meeting of the association for computational linguis- tics and the 7th international joint conference on natural language processing (volume 1: Long papers), pages 687- 696, 2015.","title":"Knowledge graph embedding via dynamic mapping matrix","authors":"Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zhao","year":"2015","venue":"Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ji et al., 2021] Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. A survey on knowl- edge graphs: Representation, acquisition, and applica- tions. IEEE transactions on neural networks and learning systems, 33(2):494-514, 2021.","title":"A survey on knowledge graphs: Representation, acquisition, and applications","authors":"Ji","year":"2021","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kazemi and Poole, 2018] Seyed Mehran Kazemi and David Poole. Simple embedding for link prediction in knowl- edge graphs. Advances in neural information processing systems, 31, 2018.","title":"Simple embedding for link prediction in knowledge graphs","authors":"Seyed Mehran, Kazemi, David Poole","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2023] Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, and Jianxin Li. Attribute- consistent knowledge graph representation learning for multi-modal entity alignment. In Proceedings of the ACM Web Conference 2023, pages 2499-2508, 2023.","title":"Attributeconsistent knowledge graph representation learning for multi-modal entity alignment","authors":"Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, Jianxin Li","year":"2023","venue":"Proceedings of the ACM Web Conference 2023","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liang et al., 2022] Ke Liang, Yue Liu, Sihang Zhou, Xin- wang Liu, and Wenxuan Tu. Relational symmetry based knowledge graph contrastive learning. ArXiv, abs/2211.10738, 2022.","title":"Relational symmetry based knowledge graph contrastive learning","authors":"Ke Liang, Yue Liu, Sihang Zhou, Xinwang Liu, Wenxuan Tu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lin et al., 2015] Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph completion. In Proceed- ings of the AAAI conference on artificial intelligence, vol- ume 29, 2015.","title":"Learning entity and relation embeddings for knowledge graph completion","authors":"Lin","year":"2015","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Loshchilov and Hutter, 2016] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. In International Conference on Learning Repre- sentations, 2016.","title":"Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts","authors":"Hutter Loshchilov","year":"2016","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Loshchilov and Hutter, 2018] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Inter- national Conference on Learning Representations, 2018.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lu et al., 2022] Xinyu Lu, Lifang Wang, Zejun Jiang, Shichang He, and Shizhong Liu. Mmkrl: A robust embed- ding approach for multi-modal knowledge graph represen- tation learning. Applied Intelligence, pages 1-18, 2022.","title":"Mmkrl: A robust embedding approach for multi-modal knowledge graph representation learning","authors":"Xinyu Lu, Lifang Wang, Zejun Jiang, Shichang He, Shizhong Liu","year":"2022","venue":"Applied Intelligence","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Lu et al., 2023a] Yuxing Lu, Xiaohong Liu, Zongxin Du, Yuanxu Gao, and Guangyu Wang. Medkpl: a heteroge- neous knowledge enhanced prompt learning framework for transferable diagnosis. Journal of Biomedical Infor- matics, 143:104417, 2023.","title":"Medkpl: a heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis","authors":"Lu","year":"2023","venue":"Journal of Biomedical Informatics","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Lu et al., 2023b] Yuxing Lu, Rui Peng, Lingkai Dong, Kun Xia, Renjie Wu, Shuai Xu, and Jinzhuo Wang. Mul- tiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes. Brief- ings in Bioinformatics, 24(6):bbad378, 2023.","title":"Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes","authors":"Lu","year":"2023","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Mohamed et al., 2021] Sameh K Mohamed, Aayah Nounu, and Vít Nováček. Biological applications of knowledge graph embedding models. Briefings in bioinformatics, 22(2):1679-1693, 2021.","title":"Biological applications of knowledge graph embedding models","authors":"Aayah Sameh K Mohamed, Vít Nounu, Nováček","year":"2021","venue":"Briefings in bioinformatics","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Nguyen et al., 2017] Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung. A novel embedding model for knowledge base completion based on convolu- tional neural network. arXiv preprint arXiv:1712.02121, 2017.","title":"A novel embedding model for knowledge base completion based on convolutional neural network","authors":"Tu Dai Quoc Nguyen, Dat Dinh Nguyen, Dinh Quoc Nguyen, Phung","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from nat- ural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark","year":"2021","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Rish and others, 2001] Irina Rish et al. An empirical study of the naive bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence, volume 3, pages 41-46, 2001.","title":"An empirical study of the naive bayes classifier","authors":"Irina Rish","year":"2001","venue":"IJCAI 2001 workshop on empirical methods in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Schlichtkrull et al., 2018] Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, et al. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings 15, pages 593-607. Springer, 2018.","title":"Modeling relational data with graph convolutional networks","authors":"Michael Schlichtkrull, Thomas N Kipf, Peter Bloem","year":"2018","venue":"The Semantic Web: 15th International Conference, ESWC 2018","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Su et al., 2012] Xiaogang Su, Xin Yan, and Chih-Ling Tsai. Linear regression. Wiley Interdisciplinary Reviews: Com- putational Statistics, 4(3):275-294, 2012.","title":"Linear regression","authors":"Xiaogang Su, Xin Yan, Chih-Ling Tsai","year":"2012","venue":"Wiley Interdisciplinary Reviews: Computational Statistics","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Sun et al., 2019] Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197, 2019.","title":"Rotate: Knowledge graph embedding by relational rotation in complex space","authors":"Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Touvron et al., 2023] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.","title":"Llama 2: Open foundation and fine-tuned chat models","authors":"Touvron","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2014] Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI conference on artificial intelligence, volume 28, 2014.","title":"Knowledge graph embedding by translating on hyperplanes","authors":"Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen","year":"2014","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2017] Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. Knowledge graph embedding: A survey of approaches and applications. IEEE transactions on knowl- edge and data engineering, 29(12):2724-2743, 2017.","title":"Knowledge graph embedding: A survey of approaches and applications","authors":"Quan Wang, Zhendong Mao, Bin Wang, Li Guo","year":"2017","venue":"IEEE transactions on knowledge and data engineering","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang et al., 2019] Zikang Wang, Linjing Li, Qiudan Li, and Daniel Zeng. Multimodal data enhanced representa- tion learning for knowledge graphs. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE, 2019.","title":"Multimodal data enhanced representation learning for knowledge graphs","authors":"Wang","year":"2019","venue":"2019 International Joint Conference on Neural Networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wishart et al., 2022] David S Wishart, AnChi Guo, Eponine Oler, Fei Wang, Afia Anjum, Harrison Peters, Raynard Di- zon, Zinat Sayeeda, Siyang Tian, Brian L Lee, et al. Hmdb 5.0: the human metabolome database for 2022. Nucleic acids research, 50(D1):D622-D631, 2022.","title":"Hmdb 5.0: the human metabolome database for 2022","authors":"Anchi David S Wishart, Eponine Guo, Fei Oler, Afia Wang, Harrison Anjum, Raynard Peters, Zinat Dizon, Siyang Sayeeda, Brian L Tian, Lee","year":"2022","venue":"Nucleic acids research","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yang et al., 2014] Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575, 2014.","title":"Embedding entities and relations for learning and inference in knowledge bases","authors":"Bishan Yang, Wen-Tau Yih, Xiaodong He, Jianfeng Gao, Li Deng","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yang et al., 2022] Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th interna- tional ACM SIGIR conference on research and develop- ment in information retrieval, pages 1434-1443, 2022.","title":"Knowledge graph contrastive learning for recommendation","authors":"Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li","year":"2022","venue":"Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yao et al., 2023] Linli Yao, Weijing Chen, and Qin Jin. Capenrich: Enriching caption semantics for web images via cross-modal pre-trained knowledge. In TheWebConf, 2023.","title":"Capenrich: Enriching caption semantics for web images via cross-modal pre-trained knowledge","authors":"Yao","year":"2023","venue":"TheWebConf","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2019] Shuai Zhang, Yi Tay, Lina Yao, and Qi Liu. Quaternion knowledge graph embeddings. Ad- vances in neural information processing systems, 32, 2019.","title":"Quaternion knowledge graph embeddings","authors":"Shuai Zhang, Yi Tay, Lina Yao, Qi Liu","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2023] Yifei Zhang, Yankai Chen, Zixing Song, and Irwin King. Contrastive cross-scale graph knowledge synergy. Proceedings of the 29th ACM SIGKDD Confer- ence on Knowledge Discovery and Data Mining, 2023.","title":"Contrastive cross-scale graph knowledge synergy","authors":"Yifei Zhang, Yankai Chen, Zixing Song, Irwin King","year":"2023","venue":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhu et al., 2022] Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, and Nicholas Jing Yuan. Multi-modal knowledge graph construction and application: A survey. IEEE Transac- tions on Knowledge and Data Engineering, 2022.","title":"Multi-modal knowledge graph construction and application: A survey","authors":"Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, Nicholas Jing Yuan","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}