Abstract
A plethora of publicly available, open scholarly data has paved the way for many applications and advanced analytics on science. However, a single dataset often contains incomplete or inconsistent records, significantly hindering its use in real-world scenarios. To address this problem, we propose a framework that allows linking scientific datasets. The resulting connections can increase the credibility of information about a given entity and serve as a link between different scholarly graphs. The outcome of this work will be used in the European Open Science Cloud (EOSC) as a base for introducing new recommendation features.
Supported by the EOSC Future project, co-funded by the EU Horizon 2020 Programme INFRAEOSC-03-2020/101017536.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Almeida, A.V.D., Borges, M.M., Roque, L.: The European open science cloud: a new challenge for Europe. In: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality. TEEM 2017, CM (2017)
Anca Hienola (ICOS), John Shepherdson (CESSDA ERIC), B.W.C.: D5.2a eosc front-office requirements analysis. Technical report (2022)
Barker, M., et al.: Digital skills for fair and open science: report from the EOSC executive board skills and training working group (2021)
Budroni, P., Claude-Burgelman, J., Schouppe, M.: Architectures of knowledge: the European open science cloud. ABI-Technik 39(2), 130–141 (2019)
Cousijn, H., et al.: Connected research: the potential of the PID graph. Patterns 2(1), 100180 (2021)
Färber, M., Ao, L.: The microsoft academic knowledge graph enhanced: author name disambiguation, publication classification, and embeddings. Quant. Sci. Stud. 3(1), 51–98 (2022)
Ferrari, T., Scardaci, D., Andreozzi, S.: The open science commons for the European research area. Earth Obs. Open Sci. Innov. ISSI Sci. Rep. Ser. 15, 43–68 (2018)
Giles, C.L.: Scholarly big data: information extraction and data mining. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 1–2 (2013)
Khan, S., Liu, X., Shakil, K., Alam, M.: A survey on scholarly data: from big data perspective. Inf. Process. Manag. 53, 923–944 (2017)
Kong, C., Gao, M., Xu, C., Qian, W., Zhou, A.: Entity matching across multiple heterogeneous data sources. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 133–146. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_9
Kong, X., Shi, Y., Yu, S., Liu, J., Xia, F.: Academic social networks: modeling, analysis, mining and applications. J. Netw. Comput. Appl. 132, 86–103 (2019)
Manghi, P., et al.: The OpenAIRE research graph data model. Zenodo (2019)
Nasar, Z., Jaffry, S.W., Malik, M.K.: Information extraction from scientific articles: a survey. Scientometrics 117(3), 1931–1990 (2018). https://doi.org/10.1007/s11192-018-2921-5
Priem, J., Piwowar, H., Orr, R.: Openalex: a fully-open index of scholarly works, authors, venues, institutions, and concepts (2022)
Roozbahani, Z., Rezaeenour, J., Shahrooei, R., et al.: Presenting a dataset for collaborator recommending systems in academic social network. J. Data, Inf. Manag. 3, 29–40 (2021). https://doi.org/10.1007/s42488-021-00041-7
Saier, T., Färber, M.: unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata. Scientometrics 125, 3085–3108 (2020). https://doi.org/10.1007/s11192-020-03382-z
Sefid, A., et al.: Cleaning noisy and heterogeneous metadata for record linking across scholarly big datasets. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9601–9606 (2019)
Shlomo, N.: Overview of data linkage methods for policy design and evaluation. In: Crato, N., Paruolo, P. (eds.) Data-Driven Policy Impact Evaluation, pp. 47–65. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-78461-8_4
Wang, L.: Heterogeneous data and big data analytics. Autom. Control Inf. Sci. 3(1), 8–15 (2017)
Wolski, M., Martyn, K., Walter, B.: A recommender system for EOSC. Challenges and possible solutions. In: Guizzardi, R., Ralyte, J., Franch, X. (eds.) Research Challenges in Information Science. RCIS 2022. LNBIP, vol. 446, pp. 70–87. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05760-1_5
Wu, J., Sefid, A., Ge, A.C., Giles, C.L.: A supervised learning approach to entity matching between scholarly big datasets. In: Proceedings of the Knowledge Capture Conference, pp. 1–4 (2017)
Wu, Z., et al.: Towards building a scholarly big data platform: challenges, lessons and opportunities. In: IEEE/ACM Joint Conference on Digital Libraries, pp. 117–126 (2014)
Xia, F., Wang, W., Bekele, T.M., Liu, H.: Big scholarly data: a survey. IEEE Trans. Big Data 3(1), 18–35 (2017)
Zhang, F., et al.: OAG: toward linking large-scale heterogeneous entity graphs. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2585–2595 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wolski, M., Klorek, A., Mazurek, C., Kobusińska, A. (2023). Linking Scholarly Datasets—The EOSC Perspective. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14073. Springer, Cham. https://doi.org/10.1007/978-3-031-35995-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-35995-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35994-1
Online ISBN: 978-3-031-35995-8
eBook Packages: Computer ScienceComputer Science (R0)