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World Wide Web - Call for Papers: Special Issue on Neuro-Symbolic Intelligence: Large Language Model Enabled Knowledge Engineering

Guest editors

  • Haofen Wang, Tongji University, China
  • Arijit Khan, Aalborg University, Denmark
  • Jun Liu, Xi'an Jiaotong University, China
  • Michael Witbrock, University of Auckland, New Zealand

The emerging neural systems, i.e., Large Language Models (LLMs), such as ChatGPT and GPT4 have revolutionized the realms of symbolic engineering techniques (i.e., Knowledge Engineering), owing to their unprecedented capabilities and broad adaptability. However, the inherent "black-box" nature of LLMs sometimes compromises their ability to consistently access accurate knowledge. Moreover, LLMs still face with the problems of producing misinformation, biased information or malicious content. On the other hand, Knowledge Graphs (KGs), the representative technique of knowledge engineering exemplified by platforms like Wikipedia and Wikidata, are structured knowledge models that offer rich, explicit, and accurate knowledge. KGs can enhance the capabilities of LLMs, offering access to external knowledge and bolstering interpretability. Yet, the dynamic nature of the world makes KGs challenging to construct, maintain and query, posing obstacles for methods that aim to handle new facts and represent emergent knowledge.

Neuro-symbolic methods attempt to integrate state-of-the-art neural techniques (e.g., LLMs) and symbolic methods (e.g., knowledge engineering) to provide a best-of-both-worlds situation and have gained increasing attention. In this special issue, we eagerly anticipate receiving original research papers, application studies, and resource (e.g., tools and datasets) submissions that delve into these compelling topics:

  • LLM techniques for symbolic knowledge extraction, alignment, and reasoning.
  • LLM techniques for symbolic knowledge querying and search.
  • Enhancing LLMs with symbolic knowledge.
  • Reasoning by LLMs and symbolic knowledge.
  • Theories of knowledge learning and storage for LLMs.
  • Knowledge editing for LLMs with KGs.
  • Explaining neural techniques with KGs.
  • Domain-specific LLMs training leveraging symbolic knowledge.
  • Cognitive and biologically-inspired neuro-symbolic agents.
  • Multimodal learning for neuro-symbolic systems.
  • Low-resource learning for neuro-symbolic systems.
  • Applications of neuro-symbolic approaches in various domains.
  • Open resources and tools combining LLMs and KGs.

Important Deadlines:

  • Manuscript Submission: 15th August 2024
  • Preliminary Decision: 15th October 2024
  • Revisions Due: 1st December 2024
  • Final Decision: 15th December 2024
  • Publication Date: 15th January 2025

Submission Guidelines

Please submit via the special issue collection page: Call for Papers: Special Issue on Neuro-Symbolic Intelligence (this opens in a new tab)

In the "Details" tab, please choose the special issue title from the collections drop down. 

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the submission guidelines on the WWWJ website.  (this opens in a new tab)

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources  (this opens in a new tab)page, including  FAQs (this opens in a new tab), Tutorials  (this opens in a new tab)along with Help and Support.  (this opens in a new tab)

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