[kdd] [CFP]Special Issue on Integrating Large Language Models and Knowledge Graphs for Generative AI, ACM Transactions on Intelligent Systems and Technology 2024

[Xie Liangru]

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            ACM Transactions on Intelligent Systems and Technology 2024
 Special Issue on Integrating Large Language Models and Knowledge Graphs
for Generative AI: Call for Papers
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###########Guest Editors:###########
    Qi He, Head of AI and VP at Nextdoor, USA, <vela1027 at gmail.com>,
https://www.linkedin.com/in/qi-he/
    Wei Wang, Leonard Kleinrock Chair Professor at UCLA, USA, <
weiwang at cs.ucla.edu>, https://web.cs.ucla.edu/~weiwang/
    Hao Wang, Professor at the Norwegian University of Science and
Technology and Xidian University, Norway and China, <iswanghao at gmail.com>,
https://www.ntnu.edu/employees/hawa

###########Special issue information:###########
    The integration of Large Language Models (LLMs) and Knowledge Graphs
(KGs) represents a cutting-edge research frontier with the potential to
revolutionize generative AI capabilities. LLMs, exemplified by GPT and
BERT, have demonstrated remarkable text and language understanding
capabilities, while KGs excel in storing factual knowledge. This synergy
holds the promise of addressing key limitations in both LLMs and KGs and
unlocking new horizons in generative AI development.
    The field of KGs has matured considerably, offering a reliable
repository for structured knowledge and facts with strong interpretability
and decision-making capabilities. However, it faces challenges in
summarizing information and generating new knowledge based on existing
correlations. In contrast, LLMs possess robust language processing and
generalization capabilities by parameterizing relationships within
knowledge, but their probabilistic nature limits interpretability and
creates hallucinations. This unique interdependence offers the opportunity
to merge their strengths, enabling the resolution of technical challenges
for generative AI, such as constructing comprehensive real-world knowledge,
and improving the accuracy of automated responses generated by chatbots.
    There are multiple key research directions in merging KGs and LLMs for
generative AI. These include enhancing the accuracy of LLMs' knowledge
summarization and generation by 1) Enhancing LLMs' accuracy in content
generation and question answering by integrating KGs within the procedures
of prompt engineering and answer retrieval; 2) Fine-tuning LLMs with KGs as
auxiliary labels; and 3) Incorporating KGs into the pre-training stages of
LLMs. Concurrently, LLMs can accelerate the construction of KGs by
converting unstructured data to structured formats, or by improving text
comprehension for tasks associated with KGs. This special issue aims to
facilitate the sharing and discussion of recent progress and future trends
in the collaborative development of LLMs and KGs for generative AI.

###########Topics:###########
    We invite submissions on all topics of Integrating Large Language
Models and Knowledge Graphs for Generative AI, including but not limited to:
            •Knowledge Graph-Enhanced LLMs: Enhanced pre-training,
inferencing, and interpretability in LLMs, along with novel applications
            •Ways in Which LLMs Enhance Knowledge Graphs: Such as knowledge
extraction, canonicalization, knowledge graph construction, ontological
schema construction, and their novel applications
            •Collaborative Approaches: Collaborations between LLMs and KGs
for bidirectional reasoning driven by both data and knowledge
            •Transforming Unstructured Data: Using LLMs to convert
unstructured data into structured data for KG generation
            •Knowledge Graph Alignment: Utilizing LLMs for alignment tasks
in knowledge graphs
            •Semantic Alignment: Investigating semantic alignment and
knowledge awareness in KG-based LLMs
            •Zero-Shot Learning: Applications of LLMs for zero-shot
learning in KGs
            •Development Potential: Exploring the potential of LLMs within
KGs and vice versa
            •Mixed Representations: Examining mixed representations of
explicit structured knowledge in KGs and parametric knowledge in LLMs
            •Evaluation and Benchmarking: Assessing specific domains using
LLMs in conjunction with KGs, along with dataset construction methods
            •Causal Reasoning: Applications of LLMs and KGs in causal
reasoning
            •Semantic Search: Utilizing LLMs and KGs for semantic search,
question answering, recommendation systems, and more
            •Fact Verification: Employing LLMs and KGs for fact
verification via reasoning
            •Complex Logical Reasoning: Investigating the use of LLMs and
KGs in complex logical reasoning tasks
            •Feature Interpretation: Exploring methods for interpreting
features in LLM-KG models

###########Important Dates:###########
            •Submissions deadline: January 31, 2024
            •First-round review decisions: April 30, 2024
            •Deadline for Minor Revision Submissions: May 31, 2024
            •Deadline for Major Revision Submissions: July 31, 2024
            •Notification of final decisions: August 31, 2024
            •Tentative publication: October 2024

###########Submission Information:###########
    Submissions must be prepared according to the TIST submission
guidelines (https://dl.acm.org/journal/tist/author-guidelines) and must be
submitted via Manuscript Central (https://mc.manuscriptcentral.com/tist).
The special issue will also consider extended versions (at least 30% new
content) of papers published at conferences.

For questions and further information, please contact Guest Editors: Qi He,
Wei Wang, Hao Wang.

More details:
https://dl.acm.org/pb-assets/static_journal_pages/tist/pdf/ACM-TIST-SI-Integrating-Large-Language-Models-Knowledge-Graphs-Generative-AI-1700078915687.pdf
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