Knowledge Graphs and Large Language Models

Dear Colleagues,

In recent years, the fields of knowledge graphs (KGs) and large language models (LLMs) have witnessed remarkable advancements, revolutionizing the landscape of artificial intelligence and natural language processing. KGs, which are structured representations of knowledge, and LLMs, which are powerful language models trained on vast amounts of text data, have individually demonstrated their prowess in various applications.

However, the combination of, and synergy between, KGs and LLMs have emerged as representing a new frontier, offering unprecedented opportunities for enhancing knowledge representation, understanding, and generation. This integration not only enriches the semantic understanding of textual data but also empowers AI systems with the ability to reason, infer, and generate contextually relevant responses.

This Special Issue aims to delve into theoretical foundations, historical perspectives, and practical applications concerning the fusion between knowledge graphs and large language models. We invite contributions that explore the following areas:

1. Theoretical Frameworks: Papers elucidating the theoretical underpinnings of the integration of KGs into LLMs, including methodologies, algorithms, and models for knowledge-enhanced language understanding and generation.
2. Historical Perspectives: Insights into the evolution of KGs and LLMs, tracing their development trajectories, seminal works, and the transformative milestones leading to their integration.
3. Design and Implementation: Research articles focusing on design principles, architectures, and techniques for effectively combining KGs and LLMs to facilitate tasks such as information retrieval, responding to questions, knowledge inference, and natural language understanding.
4. Explanatory Capabilities: Explorations into how the fusion of KGs and LLMs enables the development of explainable AI systems, providing transparent and interpretable insights into model decisions and outputs.
5. Human-Centered Intelligent Systems: Studies examining the design and deployment of interactive AI systems that leverage KGs and LLMs to facilitate seamless human–computer interactions, catering not only to experts but also to a broader, less specialized audience.

We encourage submissions that contribute to advancing our understanding of the synergistic relationship between knowledge graphs and large language models, fostering interdisciplinary collaborations across computer science, artificial intelligence, linguistics, cognitive science, and beyond. By shedding light on this burgeoning area of research, this Special Issue aims to propel the field forward and inspire further innovations in AI-driven knowledge representation and natural language processing.

Dr. Mourad Abbas
Dr. Najim Dehak
Prof. Dr. Gérard Chollet
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• knowledge graphs
• large language models
• generative and neurosymbolic AI