Apologies for crossposting.

Call for Papers

Information Processing & Management (IPM), Elsevier

CiteScore: 14.8

Impact Factor: 8.6


Guest editors:

Omar Alonso, Applied Science, Amazon, Palo Alto, California, USA. E-mail: omralon@amazon.com

Stefano Marchesin, Department of Information Engineering, University of Padua, Padua, Italy. E-mail: stefano.marchesin@unipd.it

Gianmaria Silvello, Department of Information Engineering, University of Padua, Padua, Italy. E-mail: gianmaria.silvello@unipd.it


Special Issue on “Large Language Models and Data Quality for Knowledge Graphs”

In recent years, Knowledge Graphs (KGs), encompassing millions of relational facts, have emerged as central assets to support virtual assistants and search and recommendations on the web. Moreover, KGs are increasingly used by large companies and organizations to organize and comprehend their data, with industry-scale KGs fusing data from various sources for downstream applications. Building KGs involves data management and artificial intelligence areas, such as data integration, cleaning, named entity recognition and disambiguation, relation extraction, and active learning.

However, the methods used to build these KGs involve automated components that could be better, resulting in KGs with high sparsity and incorporating several inaccuracies and wrong facts. As a result, evaluating the KG quality plays a significant role, as it serves multiple purposes – e.g., gaining insights into the quality of data, triggering the refinement of the KG construction process, and providing valuable information to downstream applications. In this regard, the information in the KG must be correct to ensure an engaging user experience for entity-oriented services like virtual assistants. Despite its importance, there is little research on data quality and evaluation for KGs at scale.

In this context, the rise of Large Language Models (LLMs) opens up unprecedented opportunities – and challenges – to advance KG construction and evaluation, providing an intriguing intersection between human and machine capabilities. On the one hand, integrating LLMs within KG construction systems could trigger the development of more context-aware and adaptive AI systems. At the same time, however, LLMs are known to hallucinate and can thus generate mis/disinformation, which can affect the quality of the resulting KG. In this sense, reliability and credibility components are of paramount importance to manage the hallucinations produced by LLMs and avoid polluting the KG. On the other hand, investigating how to combine LLMs and quality evaluation has excellent potential, as shown by promising results from using LLMs to generate relevance judgments in information retrieval.

Thus, this special issue promotes novel research on human-machine collaboration for KG construction and evaluation, fostering the intersection between KGs and LLMs. To this end, we encourage submissions related to using LLMs within KG construction systems, evaluating KG quality, and applying quality control systems to empower KG and LLM interactions on both research- and industrial-oriented scenarios.

Topics include but are not limited to:

KG construction systems

Use of LLMs for KG generation

Efficient solutions to deploy LLMs on large-scale KGs

Quality control systems for KG construction

KG versioning and active learning

Human-in-the-loop architectures

Efficient KG quality assessment

Quality assessment over temporal and dynamic KGs

Redundancy and completeness issues

Error detection and correction mechanisms

Benchmarks and Evaluation

Domain-specific applications and challenges

Maintenance of industry-scale KGs

LLM validation via reliable/credible KG data


Submission guidelines:

Authors are invited to submit original and unpublished papers. All submissions will be peer-reviewed and judged on originality, significance, quality, and relevance to the special issue topics of interest. Submitted papers should not have appeared in or be under consideration for another journal.

Papers can be submitted from 1 June 2024 to 1 September 2024. The estimated publication date for the special issue is 15 January 2025.

Papers submission via IP&M electronic submission system: https://www.editorialmanager.com/IPM

Instructions for authors: https://www.sciencedirect.com/journal/information-processing-and-management/publish/guide-for-authors

To submit your manuscript to the special issue, please choose the article type:

"VSI: LLMs and Data Quality for KGs".


More info here:

https://www.sciencedirect.com/journal/information-processing-and-management/about/call-for-papers#large-language-models-and-data-quality-for-knowledge-graphs

Important dates:

Submissions open: 1 June 2024

Submissions close: 1 September 2024

Publication date: 15 January 2025


References:

Weikum G., Dong X.L., Razniewski S., et al. (2021) Machine knowledge: creation and curation of comprehensive knowledge bases. Found. Trends Databases, 10, 108–490.

Hogan A., Blomqvist E., Cochez M. et al. (2021) Knowledge graphs. ACM Comput. Surv., 54, 71:1–71:37.

B. Xue and L. Zou. 2023. Knowledge Graph Quality Management: A Comprehensive Survey. IEEE Trans. Knowl. Data Eng. 35, 5 (2023), 4969 – 4988


G. Faggioli, L. Dietz, C. L. A. Clarke, G. Demartini, M. Hagen, C. Hauff, N. Kando, E. Kanoulas, M. Potthast, B. Stein, and H. Wachsmuth. 2023. Perspectives on Large Language Models for Relevance Judgment. In Proc. of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2023, Taipei, Taiwan, 23 July 2023. ACM, 39 – 50.


S. MacAvaney and L. Soldaini. 2023. One-Shot Labeling for Automatic Relevance Estimation. In Proc. of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023. ACM, 2230 – 2235.


X. L. Dong. 2023. Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact. Proc. VLDB Endow. 16, 12 (2023), 4130 – 4137.

S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu. 2023. Unifying Large Language Models and Knowledge Graphs: A Roadmap. CoRR abs/2306.08302 (2023).