Fetching the paper…
Reading the bibliography…
We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs).
Highly Porous and Stable Metal-Organic Frameworks: Structure Design and Sorption Properties
Mohamed Eddaoudi, Hailian Li, and O. M. Yaghi · 2000
Earlier work this paper cites.
Chebi: a database and ontology for chemical entities of biological interest
K Degtyarenko, P De Matos, M Ennis, J Hastings, M Zbinden, A Mcnaught, R Alcantara, M Darsow, M Guedj, and M Ashburner · 2007
Earlier work this paper cites.
The Reticular Chemistry Structure Resource (RCSR) Database of, and Symbols for, Crystal Nets
Michael O’keeffe, Maxim Peskov, Stuart Ramsden, and Omar Yaghi · 2008
Earlier work this paper cites.
Materials ontology: An infrastructure for exchanging materials information and knowledge
Toshihiro Ashino · 2010
Earlier work this paper cites.
Terminology of metal–organic frameworks and coordination polymers (IUPAC Recommendations 2013)
Stuart Batten, Neil Champness, Xiao-Ming Chen, Javier Garcia-Martinez, Susumu Kitagawa, Lars Öhrström, Michael O’keeffe, Myunghyun Paik Suh, and Jan Reedijk · 2013
Earlier work this paper cites.
Natural language question answering over rdf: a graph data driven approach
Lei Zou, Ruizhe Huang, Haixun Wang, Jeffrey Xu Yu, Wenqiang He, and Dongyan Zhao · 2014
Earlier work this paper cites.
Development of a cambridge structural database subset: A collection of metal–organic frameworks for past, present, and future
Peyman Moghadam, Aurelia Li, Seth Wiggin, Andi Tao, Andrew Maloney, Peter Wood, Suzanna Ward, and David Fairen-Jimenez · 2017
Earlier work this paper cites.
Mmkg: An approach to generate metallic materials knowledge graph based on dbpedia and wikipedia
Xiaoming Zhang, Xin Liu, Xin Li, and Dongyu Pan · 2017
Earlier work this paper cites.
Survey on challenges of question answering in the semantic web
Marta Sabou, Konrad Höffner, Sebastian Walter, Edgard Marx, Ricardo Usbeck, Jens Lehmann, and Axel-Cyrille Ngonga Ngomo · 2017
Earlier work this paper cites.
Lc-quad: A corpus for complex question answering over knowledge graphs
Priyansh Trivedi, Gaurav Maheshwari, Mohnish Dubey, and Jens Lehmann · 2017
Earlier work this paper cites.
9th challenge on question answering over linked data (qald-9) (invited paper)
Ricardo Usbeck, Ria Hari Gusmita, Axel-Cyrille Ngonga Ngomo, and Muhammad Saleem · 2018
Earlier work this paper cites.
Reticular chemistry in all dimensions
Omar Yaghi · 2019
Earlier work this paper cites.
Increasing topological diversity during computational “synthesis” of porous crystals: how and why
Ryther Anderson and Diego Gómez-Gualdrón · 2019
Earlier work this paper cites.
Lc-quad 2.0: A large dataset for complex question answering over wikidata and dbpedia
Mohnish Dubey, Debayan Banerjee, Abdelrahman Abdelkawi, and Jens Lehmann · 2019
Earlier work this paper cites.
A comparative survey of recent natural language interfaces for databases
Katrin Affolter, Kurt Stockinger, and Abraham Bernstein · 2019
Earlier work this paper cites.
Learning to rank query graphs for complex question answering over knowledge graphs
Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, and Jens Lehmann · 2019
Cited alongside, same era.
Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: Core mof 2019
Yongchul Chung, Emmanuel Haldoupis, Benjamin Bucior, Maciej Haranczyk, Seulchan Lee, Hongda Zhang, Konstantinos Vogiatzis, Marija Milisavljevic, Sanliang Ling, Jeffrey Camp, Ben Slater, J Siepmann, David Sholl, and Randall Snurr · 2019
Cited alongside, same era.
Identification schemes for metal–organic frameworks to enable rapid search and cheminformatics analysis
Benjamin J. Bucior, Andrew S. Rosen, Maciej Haranczyk, Zhenpeng Yao, Michael E. Ziebel, Omar K. Farha, Joseph T. Hupp, J. Ilja Siepmann, Alán Aspuru-Guzik, and Randall Q. Snurr · 2019
Cited alongside, same era.
Understanding the diversity of the metal-organic framework ecosystem
Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G. Boyd, Yongjin Lee, Berend Smit, and Heather J. Kulik · 2020
Cited alongside, same era.
propnet: A knowledge graph for materials science
Building open knowledge graph for metal-organic frameworks (mof-kg): Challenges and case studies
Yuan An, Jane Greenberg, Xintong Zhao, Xiaohua Hu, Scott McCLellan, Alex Kalinowski, Fernando J. Uribe-Romo, Kyle Langlois, Jacob Furst, Diego A. Gómez-Gualdrón, Fernando Fajardo-Rojas, and Katherine Ardila · 2022
Later among the works it cites.
Exploring pre-trained language models to build knowledge graph for metal-organic frameworks (mofs)
Yuan An, Jane Greenberg, Xiaohua Hu, Alex Kalinowski, Xiao Fang, Xintong Zhao, Scott McClellan, Fernando J. Uribe-Romo, Diego A. Gómez-Gualdrón, Kyle Langlois, Jacob Furst, Fernando Fajardo-Rojas, Katherine Ardila, Semion K. Saikin, Corey A. Harper Harper, and Ron Daniel · 2022
Later among the works it cites.
Matkg: The largest knowledge graph in materials science – entities, relations, and link prediction through graph representation learning
Vineeth Venugopal, Sumit Pai, and Elsa Olivetti · 2022
Later among the works it cites.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
David Mrdjenovich, Matthew Horton, Joseph Montoya, Christian Legaspi, Shyam Dwaraknath, Vahe Tshitoyan, Anubhav Jain, and Kristin Persson · 2020
Cited alongside, same era.
NanoMine: A Knowledge Graph for Nanocomposite Materials Science
Jamie Mccusker, Neha Keshan, Sabbir Rashid, Michael Deagen, Cate Brinson, and Deborah Mcguinness · 2020
Cited alongside, same era.
An Ontology for the Materials Design Domain
Huanyu Li, Rickard Armiento, and Patrick Lambrix · 2020
Cited alongside, same era.
Targeted classification of metal–organic frameworks in the cambridge structural database (csd)
Peyman Z. Moghadam, Aurelia Li, Xiao-Wei Liu, Rocio Bueno-Perez, Shu-Dong Wang, Seth B. Wiggin, Peter A. Wood, and David Fairen-Jimenez · 2020
Cited alongside, same era.
Knowledge graph-empowered materials discovery
Xintong Zhao, Jane Greenberg, Scott McClellan, Yong-Jie Hu, Steven Lopez, Semion K Saikin, Xiaohua Hu, and Yuan An · 2021
Cited alongside, same era.
What is in the KGQA Benchmark Datasets? Survey on Challenges in Datasets for Question Answering on Knowledge Graphs
N. Steinmetz and KU. Sattler · 2021
Cited alongside, same era.
Querying knowledge graphs in natural language
Shiqi Liang, Kurt Stockinger, Tarcisio Mendes de Farias, Maria Anisimova, and Manuel Gil · 2021
Cited alongside, same era.
A controlled vocabulary and metadata schema for materials science data discovery
Andrea Medina-Smith, Chandler Becker, Raymond Plante, Laura Bartolo, Alden Dima, James Warren, and Robert Hanisch · 2021
Cited alongside, same era.
A deep neural approach to kgqa via sparql silhouette generation
Sukannya Purkayastha, Saswati Dana, Dinesh Garg, Dinesh Khandelwal, and G.P Shrivatsa Bhargav · 2022
Later among the works it cites.
Sgpt: A generative approach for sparql query generation from natural language questions
Md Rashad Al Hasan Rony, Uttam Kumar, Roman Teucher, Liubov Kovriguina, and Jens Lehmann · 2022
Later among the works it cites.
Prompt design and answer processing for knowledge base construction from pre-trained language models (lm-kbc)
Xiao Fang, Alexander Kalinowski, Haoran Zhao, Ziao You, Yuhao Zhang, and Yuan An · 2022
Later among the works it cites.
Elementary Multiperspective Material Ontology (EMMO)
EMMO · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
Later among the works it cites.
A deep neural approach to kgqa via sparql silhouette generation
Sukannya Purkayastha, Saswati Dana, Dinesh Garg, Dinesh Khandelwal, and G.P Shrivatsa Bhargav · 2022
Later among the works it cites.
Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT
Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao · 2023
Closest in time.
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Xuanting Chen, Junjie Ye, Can Zu, Nuo Xu, Rui Zheng, Minlong Peng, Jie Zhou, Tao Gui, Qi Zhang, and Xuanjing Huang · 2023
Closest in time.
Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour · 2023
Closest in time.
Evaluation of ChatGPT as a Question Answering System for Answering Complex Questions
Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen, and Guilin Qi · 2023
Closest in time.