Organization of Information and Knowledge in the Big Data Environment

Call for papers for: The Electronic Library

New Directions, Challenges, and Opportunities for Library Practice, Research, and Education


Submission deadline: 31st October 2020

Guest editors:

Oksana L. Zavalina

Email: [email protected]

 Xiaoguang Wang

Email: [email protected]

Qikai Cheng

Email: [email protected]  

Overview of special issue

In the recent decade, the explosion of information that has become Big Data offers both opportunities and challenges for organizing and providing efficient access to it. Knowledge graphs – used increasingly in education (e.g., Wang et al., 2018), law (e.g., Herring and Meyer, 2018), and medicine (e.g., Deng et al., 2018), as well as other domains – is one example of a tool resulting from organizing Big Data. High quality knowledge graphs help produce valuable discoveries, thus an examination of knowledge graph quality from intrinsic and extrinsic perspectives gains importance (e.g., Paulheim, 2017). With the growing availability of digital data on scholarly activities, there is an increased need for semantic analysis to help ensure that machines can understand the concepts behind academic language and mine valuable knowledge. Semantic role labeling (SRL) – a natural language processing task that aims to locate all arguments for a given predicate in a sentence and label them with semantic roles – is important for helping meet this need and support information retrieval, information extraction, question answering, and machine translation. The need for SRL to focus on scholarly Big Data in a variety of domains has been identified (e.g., Dahlmeier and Ng, 2010).

The continuous development of information technologies greatly affects the development of digital libraries. The Big Data environment presents challenges to organizing digital and non-digital information for access; for example, in the digital humanities field (Tomasi, 2018). Digital libraries realize the need to develop a more solid understanding of research data and to overcome the "cyberinfrastructural challenge" to be able to adequately support curation, sharing, and reuse of data generated by data-intensive research (Salo, 2010; Xie and Fox, 2017). The role of libraries in Big Data has been assessed in several recent studies (Zhan and Widén, 2018).

The synergy between library and information science and data science emerges to address these Big Data challenges and opportunities. Some of the most fruitful areas of such collaboration have been identified within the theory and practice of information organization, and knowledge organization (as defined in the library and information science fields). For example, "big metadata, smart metadata", and leveraging the "metadata capital" have been named as important areas of research (Greenberg, 2017). Major conferences, such as the Annual Meeting of the Association for Information Science and Technology (2018) and the Joint Conference on Digital Libraries (2019, 2020) held workshops focusing on knowledge organization, information organization (with metadata in particular) in the Big Data and the Semantic Web environments. The challenges and opportunities that the Big Data environment brings for metadata and, more broadly, information organization and knowledge organization, affect not only the research and practical work of libraries and other memory institutions (archives and museums), but also, importantly, library and information science education.

This special issue topic is Organization of Information and Knowledge in the Big Data Environment: New Directions, Challenges, and Opportunities for Library Practice, Research, and Education. The special issue aims to investigate novel and innovative approaches to research, practice, and teaching that would help libraries meet the challenges in the Big Data environment. The special issue seeks to explore solutions to facilitating access to data, information, and knowledge at a large scale, through information organization and knowledge organization (as defined by the International Society for Knowledge Organization,

Indicative list of anticipated themes

  • Analysis of methods and technologies (e.g., algorithms, Semantic Web tools) for information organization (e.g., metadata), as well as for organizing knowledge (e.g., knowledge organizations systems) in the Big Data environment;
  • Theoretical developments to support information organization and knowledge organization in the Big Data environment;
  • Education endeavors to support organization of information and knowledge in the Big Data environment: opportunities and challenges to formal and continuous education in these areas that Big Data poses, as well as solutions developed or under development to address them;
  • Comparative analyses of organization of information organization and knowledge in different contexts;
  • Large-scale automatic metadata extraction and information modeling approaches: domain-specific or domain-independent;
  • Employing Big Data analytics approaches in metadata quality evaluation and quality control, digital library use analysis, and so on;
  • Evaluations of the application of existing and proposed new knowledge graphs to information retrieval, question answering, and other tasks;
  • Semantic role labeling of scholarly Big Data.

Key dates

Paper submission deadline: 31st October

Author notification:15th January 2021

Revised papers submission: 15th March 2021

Final acceptance:15th May 2021

Submission details

View the author guidelines on the journal homepages

Please submit your manuscript via our review website


Dahlmeier, D. and Ng, H.T. (2010), “Domain adaptation for semantic role labeling in the biomedical domain”, Bioinformatics, Vol. 26 No. 8, pp. 1098-1104.

Deng, Y., Li, Y., Du, N., Fan, W., Shen, Y. and Lei, K. (2019), “When truth discovery meets medical knowledge graph: Estimating trustworthiness degree for medical knowledge condition”, paper presented at the 28th Conference on Information and Knowledge Management (CIKM ‘19), November, Beijing, China, available at: (accessed 12 August 2020).

Greenberg, J. (2017), “Big metadata, smart metadata, and metadata capital: Toward greater synergy between data science and metadata”, Journal of Data and Information Science, Vol. 2 No. 3, available at: (accessed 12 August 2020).

Herring, J., Cavar, D. and Meyer, A. (2018), “Case law analysis using deep NLP and knowledge graphs”, in Rehm, G., Rodríguez-Doncel, V. and Moreno-Schneider, J. (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC ‘18), 12 May, Miyazaki, Japan, European Language Resources Association (ELRA), Paris, pp. 1-6, available at: (accessed 12 August 2020).

Paulheim, H. (2017), “Knowledge graph refinement: A survey of approaches and evaluation methods”, Semantic Web, Vol. 8, pp. 489-508.

Salo, D. (2010), “Retooling libraries for the data challenge”, Ariadne, Vol. 64, available at: (accessed 12 August 2020).

Tomasi, F. (2018), “Modelling in the digital humanities: Conceptual data models and knowledge organization in the cultural heritage domain”, Historical Social Research/Historische Sozialforschung. Supplement 31, pp. 170-179, available at: (accessed 12 August 2020).

Wang, R., Yan, Y., Wang, J., Jia, Y., Zhang, Y., Zhang, W. and Wang, X. (2018), “AceKG: A large-scale knowledge graph for academic data mining”, paper presented at the 27th ACM International Conference on Information and Knowledge Management (CIKM ‘18), 22-26 October, Torino, Italy, pp. 1487-1490, available at: (accessed 12 August 2020).

Xie, Z. and Fox, E. (2017), “Advancing library cyberinfrastructure for big data sharing and reuse”, Information Services Use, Vol. 37 No. 3, pp. 319-323, available at: (accessed 12 August 2020).

Zhan, M. and Widén, G. (2018), “Public libraries: Roles in Big Data”, The Electronic Library, Vol. 36 No. 1, pp. 133-145, available at: (accessed 12 August 2020).