Apologies for crossposting.
Call for Papers
Information Processing & Management (IPM), Elsevier
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CiteScore: 14.8
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Impact Factor: 8.6
Guest editors:
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Omar Alonso, Applied Science, Amazon, Palo Alto, California, USA. E-mail:
omra...@amazon.com
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Stefano Marchesin, Department of Information Engineering, University of
Padua, Padua, Italy. E-mail: stefano.marche...@unipd.it
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Gianmaria Silvello, Department of Information Engineering, University
of Padua, Padua, Italy. E-mail: gianmaria.silve...@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:
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KG construction systems
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Use of LLMs for KG generation
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Efficient solutions to deploy LLMs on large-scale KGs
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Quality control systems for KG construction
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KG versioning and active learning
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Human-in-the-loop architectures
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Efficient KG quality assessment
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Quality assessment over temporal and dynamic KGs
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Redundancy and completeness issues
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Error detection and correction mechanisms
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Benchmarks and Evaluation
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Domain-specific applications and challenges
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Maintenance of industry-scale KGs
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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:
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Submissions open: 1 June 2024
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Submissions close: 1 September 2024
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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).
--
Stefano Marchesin, PhD
Assistant Professor (RTD/a)
Information Management Systems (IMS) Group
Department of Information Engineering
University of Padua
Via Gradenigo 6/a, 35131 Padua, Italy
Home page: http://www.dei.unipd.it/~marches1/
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