Fetching the paper…
Reading the bibliography…
Taking advantage of the widespread use of ontologies to organise and harmonize knowledge across several distinct domains, this paper proposes a novel approach to improve an embedding-Large Language Model (embedding-LLM) of interest by infusing the knowledge formalized by a reference ontology: ontological knowledge infusion aims at boosting the ability of the considered LLM to effectively model the knowledge domain described by the infused ontology.
The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider · 2004
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Semeval-2012 task 6: A pilot on semantic textual similarity.* sem 2012: The first joint conference on lexical and computational semantics—
Eneko Agirre, Daniel Cer, Mona Diab, and Aitor Gonzalez-Agirre · 2012
Earlier work this paper cites.
* sem 2013 shared task: Semantic textual similarity
Eneko Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, and Weiwei Guo · 2013
Earlier work this paper cites.
Semeval-2014 task 10: Multilingual semantic textual similarity
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe · 2014
Earlier work this paper cites.
Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, et al · 2015
Earlier work this paper cites.
Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation
Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez Agirre, Rada Mihalcea, German Rigau Claramunt, and Janyce Wiebe · 2016
Earlier work this paper cites.
On sampling strategies for neural network-based collaborative filtering
Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong · 2017
Earlier work this paper cites.
Biosses: a semantic sentence similarity estimation system for the biomedical domain
Gizem Soğancıoğlu, Hakime Öztürk, and Arzucan Özgür · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
Machine learning with biomedical ontologies
Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, and Robert Hoehndorf · 2020
Cited alongside, same era.
Self-alignment pretraining for biomedical entity representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, and Nigel Collier · 2020
Cited alongside, same era.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Cited alongside, same era.
Generating datasets with pretrained language models
Timo Schick and Hinrich Schütze · 2021
Towards general text embeddings with multi-stage contrastive learning
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang · 2023
Later among the works it cites.
Ontology engineering with large language models
Patricia Mateiu and Adrian Groza · 2023
Later among the works it cites.
Ontochatgpt information system: Ontology-driven structured prompts for chatgpt meta-learning
Oleksandr Palagin, Vladislav Kaverinskiy, Anna Litvin, and Kyrylo Malakhov · 2023
Later among the works it cites.
Sncse: Contrastive learning for unsupervised sentence embedding with soft negative samples
Hao Wang and Yong Dou · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Nilinker: attention-based approach to nil entity linking
Pedro Ruas and Francisco M Couto · 2022
Cited alongside, same era.
One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu · 2022
Cited alongside, same era.
Mondo: Unifying diseases for the world, by the world
Nicole A Vasilevsky, Nicolas A Matentzoglu, Sabrina Toro, Joseph E Flack IV, Harshad Hegde, Deepak R Unni, Gioconda F Alyea, Joanna S Amberger, Larry Babb, James P Balhoff, et al · 2022
Cited alongside, same era.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
Cited alongside, same era.
Linkbert: Pretraining language models with document links
Michihiro Yasunaga, Jure Leskovec, and Percy Liang · 2022
Cited alongside, same era.
Fine-tuning large enterprise language models via ontological reasoning
Teodoro Baldazzi, Luigi Bellomarini, Stefano Ceri, Andrea Colombo, Andrea Gentili, and Emanuel Sallinger · 2023
Cited alongside, same era.
A survey of knowledge enhanced pre-trained language models
Linmei Hu, Zeyi Liu, Ziwang Zhao, Lei Hou, Liqiang Nie, and Juanzi Li · 2023
Cited alongside, same era.
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei · 2023
Later among the works it cites.
Increasing the llm accuracy for question answering: Ontologies to the rescue!
Dean Allemang and Juan Sequeda · 2024
Closest in time.
Large language models as oracles for instantiating ontologies with domain-specific knowledge
Giovanni Ciatto, Andrea Agiollo, Matteo Magnini, and Andrea Omicini · 2024
Closest in time.
Data augmentation using llms: Data perspectives, learning paradigms and challenges
Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, and Shafiq Joty · 2024
Closest in time.
A comprehensive overview of ontology: Fundamental and research directions
Archana Patel and Narayan C Debnath · 2024
Closest in time.
Gistembed: Guided in-sample selection of training negatives for text embedding fine-tuning
Aivin V Solatorio · 2024
Closest in time.
Large language models for data annotation: A survey
Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, and Huan Liu · 2024
Closest in time.
Give us the facts: Enhancing large language models with knowledge graphs for fact-aware language modeling
Linyao Yang, Hongyang Chen, Zhao Li, Xiao Ding, and Xindong Wu · 2024
Closest in time.