2023

The Semantic Scholar Open Data Platform

Kinney, Rodney, Anastasiades, Chloe, Authur, Russell et al.

Understand

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.

Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature.
We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date, with 200M+ papers, 80M+ authors, 550M+ paper-authorship edges, and 2.4B+ citation edges.
The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.

Built on

An Overview of Microsoft Academic Service (MAS) and Applications
Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Paul Hsu, and Kuansan Wang. 2015 · 2015
Earlier work this paper cites.
Identifying Meaningful Citations. In AAAI Workshop: Scholarly Big Data
Marco Valenzuela, Vu A. Ha, and Oren Etzioni. 2015 · 2015
Earlier work this paper cites.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In NIPS
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017 · 2017
Earlier work this paper cites.
Construction of the Literature Graph in Semantic Scholar. In NAACL
Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu A. Ha, Rodney Michael Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler C. Murray, Hsu-Han Ooi, Matthew E. Peters, Joanna L. Power, Sam Skjonsberg, Lucy Lu Wang, Christopher Wilhelm, Zheng Yuan, Madeleine van Zuylen, and Oren Etzioni. 2018 · 2018
Earlier work this paper cites.
SciBERT: A Pretrained Language Model for Scientific Text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Association for Computational Linguistics, Hong Kong, China, 3615–3620
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Earlier work this paper cites.
Structural Scaffolds for Citation Intent Classification in Scientific Publications
Arman Cohan, Waleed Ammar, Madeleine van Zuylen, and Field Cady. 2019 · 2019
Earlier work this paper cites.

Similar

Parameter-Efficient Transfer Learning for NLP. In Proceedings of the 36th International Conference on Machine Learning
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Original
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
PubLayNet: largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) . IEEE, 1015–1022
Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. 2019 · 2019
Cited alongside, same era.
TLDR: Extreme Summarization of Scientific Documents. In FINDINGS
Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel S. Weld. 2020 · 2020
Cited alongside, same era.
SPECTER: Document-level Representation Learning using Citation-informed Transformers
Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S. Weld. 2020 · 2020
Cited alongside, same era.
S2ORC: The Semantic Scholar Open Research Corpus. In ACL
Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Michael Kinney, and Daniel S. Weld. 2020 · 2020
Cited alongside, same era.

Then

Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 10781–10790
Mingxing Tan, Ruoming Pang, and Quoc V Le. 2020 · 2020
Later among the works it cites.
LayoutParser: A unified toolkit for deep learning based document image analysis. In International Conference on Document Analysis and Recognition . Springer, 131–146
Zejiang Shen, Ruochen Zhang, Melissa Dell, Benjamin Charles Germain Lee, Jacob Carlson, and Weining Li. 2021 · 2021
Later among the works it cites.
S2AND: A Benchmark and Evaluation System for Author Name Disambiguation
Original
Shivashankar Subramanian, Daniel King, Doug Downey, and Sergey Feldman. 2021 · 2021
Later among the works it cites.
VILA: Improving structured content extraction from scientific PDFs using visual layout groups
Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S Weld, and Doug Downey. 2022 · 2022
Later among the works it cites.
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 5548–5566
Amanpreet Singh, Mike D’Arcy, Arman Cohan, Doug Downey, and Sergey Feldman. 2023 · 2023
Closest in time.
Open Source Strikes Bread - New Fluffy Embeddings Model
Sean Lee, Aamir Shakir, Darius Koenig, and Julius Lipp. 2024 · 2024
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…