Iana Atanassova
Marc Bertin
Philipp Mayr- 1CRIT—Université de Bourgogne Franche-Comté, Besançon, France
- 2Institut Universitaire de France (IUF), Paris, France
- 3ELICO—Université Claude Bernard Lyon 1, Lyon, France
- 4GESIS—Leibniz-Institute for the Social Sciences, Cologne, Germany
Editorial on the Research Topic
Mining Scientific Papers, Volume II: Knowledge Discovery and Data Exploitation
1. Introduction
The Research Topic on “Knowledge Discovery and Data Exploitation” aims at promoting interdisciplinary research in computational linguistics and in Natural Language Processing (NLP) applied to the fields of Bibliometrics, Scientometrics, and Information Retrieval. It is a follow-up of our previous Research Topic: “Mining Scientific Papers: NLP-enhanced Bibliometrics” (Atanassova et al., 2019).
The processing of scientific texts, which includes the analysis of citation contexts but also the task of information extraction from scientific papers for various applications, has been the object of intensive research during the last decade. This has become possible thanks to two factors. The first one is the growing availability of scientific papers in full text and in machine-readable formats together with the rise of the Open Access publishing of papers on online platforms such as ArXiv, Semantic Scholar, CiteSeer, or PLOS. The second factor is the relative maturity of open source tools and libraries for natural language processing that facilitate text processing (e.g., Spacy, NLTK, Mallet, OpenNLP, CoreNLP, Gate, CiteSpace). As a result, a large number of experiments have been conducted by processing the full text of papers for citation context analysis, but also summarization and recommendation of scientific papers.
This Research Topic aims to discuss novel approaches that focus on the processing and exploitation of data extracted from scientific literature. In particular, the possibility to enrich metadata by the full-text processing of papers offers new fields of investigation that are related to the representation of data and the production of knowledge by the aggregation of data from multiple documents. Given the wide range of available techniques, several questions arise in this field: What volume of scientific data should be considered exploitable and allow the production of new knowledge through aggregation? How can knowledge generated from data in scientific articles be represented? What types of data and knowledge can be automatically extracted from scientific articles and how can it be exploited efficiently?
2. Papers in This Research Topic
The six papers published in this Research Topic were all reviewed by at least two independent reviewers who have been assigned by the editors.
In the paper “Language Bias in Health Research: External Factors That Influence Latent Language Patterns” Valdez and Goodson the authors propose to use topic modeling to study the linguistic properties of abstracts of papers in Health research and predict language bias. The paper analyses the language alterations according to three factors: time, funding sources and nation of origin. The results show that each of these three factors influence the linguistic patterns used in the abstracts of papers.
The paper titled “Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks” Kandimalla et al. propose a method for classifying scientific articles based on their abstracts. For this purpose, the authors propose to use a deep attentive neural network (DANN) trained on abstracts obtained from the Web of Science (WoS) and its categories. The results obtained are better than existing approaches based on clustering and citation networks.
The paper “SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing” van Haastrecht et al. introduce an innovative systematic review methodology, called SYMBALS. SYMBALS blends the traditional method of backward snowballing with the machine learning method of active learning. The authors proved the validity of their method using a replication study with ASReview, where SYMBALS could accelerate the title and abstract screening.
The opinion paper “Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata” Turki et al. elaborates on how each type of bibliographic metadata can provide useful insights to enhance the automatic enrichment and fact-checking of knowledge graphs from scholarly publications. The authors explore about research efforts connected to the Bibliometric-enhanced Information Retrieval initiative (Cabanac et al., 2020a,b).
The paper “Visual Summary Identification From Scientific Publications via Self-Supervised Learning” Yamamoto et al. builds a novel benchmark data set for visual summary identification from scientific publications, which consists of papers presented at computer science conferences. The authors introduce and evaluated a new self-supervised learning approach to learn a heuristic matching of in-text references to figures with figure captions.
The paper “NLP4NLP+5: The Deep (R)evolution in Speech and Language Processing” Mariani et al. continues the series of two papers that were published on the NLP4NLP corpus in our previous Research Topic. This new paper uses similar methods, but adds to the dataset 5 more years of publications, between 2016 and 2020. Research in the field of Speech and Language Processing during these years has been intense and some significant evolution in the Research Topics can be observed. The analysis of the dataset shows that large communities have shifted their research to novel topics such as Neural Networks and Word Embeddings. This, together with the acceleration of the publication process and the growth in the use of language resources, account for some important transformations in this field of research. The authors provide a thorough analysis of the dataset that shows these phenomena.
3. Conclusion
The topic of mining scientific papers, and more broadly text mining methods used in the fields of NLP-enhanced Bibliometrics and knowledge discovery, generate much interest from the community. At the moment of publication of this editorial, the two Research Topics on Mining Scientific Papers Vol 1 NLP-enhanced Bibliometrics1 and Vol 2 Knowledge Discovery and Data Exploitation2 have attracted more than 99,000 and 24,000 views respectively.
The set of papers that were published in the two Research Topics show various methods that were applied to the full text of articles, or their metadata, references and abstracts. The Table 1 presents an overview of all 13 papers that were published. This table shows the variety of topics and areas of applications that were addressed, as well as the objects, corpora and methods that were used (the table scheme was copied from Cabanac et al., 2020b).
Table 1. Overview of the articles in the RTs Vol 1/Vol 2.
Author Contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
The work by IA was partly funded by the French ANR project InSciM Modeling Uncertainty in Science ANR-21-CE38-0003-01. The work by MB was partly funded by the French ANR project TheoScit Study of citation contexts for a construction of semantic relational indicators ANR-20-CE38-0003-01.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
We wish to thank all contributors to these two Research Topics: The researchers who submitted papers and especially the reviewers who generously offered their time and expertise. Since 2016, we have been maintaining the Bibliometric-enhanced-IR Bibliography3 which is a bibliography of all scientific articles (workshops and journals) on this Research Topic.
Footnotes
1. ^https://www.frontiersin.org/research-topics/7043/mining-scientific-papers-nlp-enhanced-bibliometrics
2. ^https://www.frontiersin.org/research-topics/13388/mining-scientific-papers-volume-ii-knowledge-discovery-and-data-exploitation
3. ^https://github.com/PhilippMayr/Bibliometric-enhanced-IR_Bibliography/
References
Atanassova, I., Bertin, M., and Mayr, P. (2019). Editorial: mining scientific papers: NLP-enhanced bibliometrics. Front. Res. Metr. Analyt. 4:911070. doi: 10.3389/frma.2022.911070
Cabanac, G., Frommholz, I., and Mayr, P. (2020a). “Bibliometric-enhanced information retrieval 10th anniversary workshop edition,” in Advances in Information Retrieval, Volume 12036, eds J. M. Jose, E. Yilmaz, J. Magalhes, P. Castells, N. Ferro, M. J. Silva, and F. Martins (Cham: Springer International Publishing), 641–647.
Cabanac, G., Frommholz, I., and Mayr, P. (2020b). Scholarly literature mining with information retrieval and natural language processing: preface. Scientometrics 125, 2835–2840. doi: 10.1007/s11192-020-03763-4
Ermakova, L., Bordignon, F., Turenne, N., and Noel, M. (2018). Is the abstract a mere teaser? Evaluating generosity of article abstracts in the environmental sciences. Front. Res. Metr. Analyt. 3:16. doi: 10.3389/frma.2018.00016
He, J., and Chen, C. (2018). Temporal representations of citations for understanding the changing roles of scientific publications. Front. Res. Metr. Analyt. 3:27. doi: 10.3389/frma.2018.00027
Mariani, J., Francopoulo, G., and Paroubek, P. (2019a). The NLP4NLP Corpus (I): 50 years of publication, collaboration and citation in speech and language processing. Front. Res. Metr. Analyt. 3:36. doi: 10.3389/frma.2018.00036
Mariani, J., Francopoulo, G., Paroubek, P., and Vernier, F. (2019b). The NLP4NLP Corpus (II): 50 Years of Research in Speech and Language Processing. Front. Res. Metr. Analyt. 3:37. doi: 10.3389/frma.2018.00037
Meyers, A. L., He, Y., Glass, Z., Ortega, J., Liao, S., Grieve-Smith, A., et al. (2018). The termolator: terminology recognition based on chunking, statistical and search-based scores. Front. Res. Metr. Analyt. 3:3:19. doi: 10.3389/frma.2018.00019
Nomoto, T.. (2018). Resolving citation links with neural networks. Front. Res. Metr. Analyt. 3:31. doi: 10.3389/frma.2018.00031
Keywords: text mining, scientific papers, scientometrics, natural language processing, computational linguistics, citation content analysis, academic search
Citation: Atanassova I, Bertin M and Mayr P (2022) Editorial: Mining Scientific Papers, Volume II: Knowledge Discovery and Data Exploitation. Front. Res. Metr. Anal. 7:911070. doi: 10.3389/frma.2022.911070
Received: 01 April 2022; Accepted: 23 May 2022;
Published: 15 June 2022.
Edited and reviewed by: Min Song, Yonsei University, South Korea
Copyright © 2022 Atanassova, Bertin and Mayr. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Iana Atanassova, iana.atanassova@univ-fcomte.fr