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2019-10-22 10:22:35
2019-10-22
Indexed in: SCI Expanded, EI, etc.
https://www.springer.com/journal/11390
CALL FOR PAPERS
Special Section on Entity Resolution
Aims and Scope
Entity Resolution (ER) is the task of disambiguating manifestations of real-world entities in various records or mentions by linking and grouping. It has been recognized as a key process for improving data quality in data integration of modern information systems. ER covers a wide range of approaches to entity-based integration, known variously as record linkage, de-duplication, heterogeneous join, identity resolution, reference matching, etc.
During the recent decade, ER has developed beyond the traditional task of integrating database records, and has witnessed innovations in various domains such as social media, multimedia, location-based services, etc. For example, many researchers have focused on cross-platform linking based on the temporal evolution of attributes or the spatiotemporal behavior of objects. In addition, with the wide adoption and technological advancement of deep learning, much effort has been devoted to utilizing deep learning models for exploring the hidden behavior of entities and thus enhancing the accuracy of linking. This can also be combined with the crowd power for further improvement. Moreover, ER has encountered a great challenge in the big data era to guarantee its efficiency, considering the prohibitively expensive operation of pairwise entity comparison, which requires new techniques to be developed and modern computing platforms to be utilized for efficient ER.
Recognizing the growing impact of ER on information quality in organizations, and the new challenges and opportunities of ER in the big data era, the Journal of Computer Science & Technology (JCST) will devote a special section to innovative high-quality research papers in this area. This special section of JCST journal papers will focus on technologies and solutions related, but not limited to:
Temporal/spatiotemporal entity resolution;
Multi-modal entity resolution;
Cross-domain entity resolution;
Deep learning approaches to entity resolution;
Entity resolution and knowledge graphs;
Entity resolution and crowdsourcing;
Efficiency issues on large-scale entity resolution;
Blocking techniques for entity resolution;
Entity resolution and high-performance computing;
Entity resolution in special application domains and context;
Benchmarking for entity resolution.
Besides original research papers, we also strongly encourage high-quality survey papers, systems papers and applications papers.
Schedule
Submission due: January 15, 2020
First review completed: February 25, 2020
Revision due: March 25, 2020
Final decision: April 30, 2020
Final manuscript due: May 9, 2020
Expected publication: July 2020
Submission Procedure
All submissions must be done electronically through JCST's e-submission system at https://mc03.manuscriptcentral.com/jcst, with a manuscript type: "Special Section on Entity Resolution".
Leading Editor
Xiaofang Zhou (The University of Queensland, Australia)
Guest Editor
Wen Hua (The University of Queensland, Australia)
