Overview

We are excited to announce the first International OpenKG Workshop on Large Knowledge-enhanced Models (LKM2024) held in conjunction with IJCAI 2024. The workshop aims to bring together researchers from academia and industry to discuss the latest advances and challenges on a variety of topics over knowledge-enhanced large language models in AI, and the integration of large models with symbolic KR such as KG.

Humankind accumulates knowledge about the world in the processes of perceiving the world, with natural languages as the primary carrier of world knowledge. Representing and processing these world knowledge has been central to its objectives since the early advent of AI. Indeed, both LLMs and KGs were developed to handle world knowledge but exhibit distinct advantages and limitations. LLMs excel in language comprehension and offer expansive coverage of knowledge, but incur significant training costs and struggle with authenticity problems of logic reasoning. KGs provide highly accurate and explicit knowledge representation, enabling more controlled reasoning and being immune from hallucination problems, but face scalability challenges and struggle with reasoning transferability. A deeper integration of these two technologies promises a more holistic, reliable, and controllable approach to knowledge processing in AI.

Natural languages merely encode world knowledge through sequences of words, while human cognitive processes extend far beyond simple word sequences. Considering the intricate nature of human knowledge, we advocate for the research over Large Knowledge-enhanced Models (LKM), specifically engineered to manage diversified spectrum of knowledge structures. In this workshop, we focus on exploring large models through the lens of “knowledge”. We expect to investigate the role of symbolic knowledge such as Knowledge Graphs (KGs) in enhancing LLMs, and also interested in how LLMs can amplify traditional symbolic knowledge bases.

Call for Papers

Call for Papers

We welcome all submissions related to but not limited to the following topics:

  • Large model knowledge enhancement
  • Integration of LLM and symbolic KR
  • Knowledge-injecting LLM pretraining
  • Structure-inducing LLM pre-training
  • Knowledge-augmented prompt learning
  • Knowledge-enhanced instruction learning
  • Graph RAG and KG RAG
  • LLM-enhanced symbolic query and reasoning
  • Large model knowledge extraction
  • Large model knowledge editing
  • Large model knowledge reasoning
  • Knowledge-augmented multi-modal large models
  • Multimodal learning for KGs and LLMs
  • Knowledge-enhanced Hallucination Detection and Mitigation
  • Semantic tools for LLMs
  • Knowledgeable AI agents
  • Integration of LLM and KG for world models
  • Domain-specific LLMs training leveraging KGs
  • Applications of combing KGs and LLMs
  • Open resources combining KGs and LLMs

Submission URL: https://cmt3.research.microsoft.com/LKM2024

Format: Submissions are invited in the form of 7-page papers (with an additional 2 pages for references) for inclusion in the proceedings, or a 2-page abstract for poster and demonstration proposals. All submissions must adhere to the formatting requirements specified in the conference's author guidelines, available at https://www.ijcai.org/authors_kit. Submissions will be reviewed in a single-blind manner, and it is required to include all authors' names and affiliations. Papers that fail to adhere to the submission guidelines or fall outside the scope of the workshop's relevant topics will be desk rejected. Accepted papers will be featured in the workshop program and incorporated into the workshop proceedings, although authors may choose to opt out of this inclusion.

Dual-submission policy: We welcome ongoing and unpublished work. We also welcome papers that are under review at the time of submission, or that have been recently accepted. We welcome both accepted and rejected IJCAI 2024 papers for submission to this workshop.

Archival/Non-archival: The accepted papers may choose between two publication options: archival or non-archival. All archival papers ( excluding 2-page abstract for poster and demonstration proposals) will be recommended to a special issue in Data Intelligence Journal (https://direct.mit.edu/dint). Selected archival papers will be invited to submit extensional versions to the Elsevier Journal of Big Data Research (SCI Indexed). To qualify for archival publication, submissions must be notably original and not previously published in other venues or journals. We will have a new round of peer review for both journals, the peer review process is not much different from regular journal submissions. We will proceed with the subsequent steps as quickly as possible. The final acceptance of the journal will be determined by the outcome of new round of review. Non-archival papers, on the other hand, are permitted to be works that have been presented or published in another venue or journal.

Presentation: accepted papers can choose between an in-person presentation or opting out of an onsite presentation.

The workshop is organized by OpenKG, an open research community committed to the innovation on open technologies for KGs and their integration with modern large language models.

Important Dates:

  • Submission deadline: Jun 1, 2024 AOE
  • Notification to authors: Jun 7, 2024
  • Camera-ready deadline: July 15, 2024 AOE

Speakers

Ying Ding

Bill & Lewis Suit Professor School of Information University of Texas at Austin

Quanming Yao

Department of Electronic Engineering, Tsinghua University

Wenpeng Yin

Institute for Computational and Data Sciences at Penn State

Ning Xu

School of Computer Science and Engineering, College of Software Engineering, Southeast University

Cheng Yang

Beijing University of Posts and Telecommunications

Lei Liang

Ant Group

Schedule

Time Topics Duration Speakers
9:00AM - 9:20AM Opening Remarks 20 mins Huajun Chen
9:20AM - 10:00AM KeyNote #1 40 mins Ying Ding
10:00AM - 10:30AM Invited Talk #1 30 mins Quanming Yao
10:30AM - 11:00AM Coffee Break & Poster Session I 30 mins
11:00AM - 11:30AM Invited Talk #2 30 mins Wenpeng Yin
11:30AM - 12:00AM Invited Talk #3 30 mins Lei Liang
Panel (Ying Ding, Huajun Chen, Quanming Yao, Wenpeng Yin, Lei Liang) Ningyu Zhang
Lunch Break & Poster Session II
14:00PM - 14:30PM Invited Talk #4 30 mins Ning Xu
14:30PM - 15:00PM Invited Talk #5 30 mins Cheng Yang
15:00PM - 15:30PM Invited Talk #6 30 mins Shumin Deng
15:30PM - 16:00PM Coffee Break & Poster Session III 30 mins
16:00PM - 16:40PM KeyNote #2 40 mins Jeff Z. Pan
16:40PM - 16:50PM Spotlight Presentation #1 10 mins
16:50PM - 17:00PM Spotlight Presentation #2 10 mins
17:00PM - 17:10PM Spotlight Presentation #3 10 mins
17:10PM - 17:20PM Spotlight Presentation #4 10 mins
17:20PM - 17:30PM Spotlight Presentation #5 10 mins
17:30PM - 17:35PM Closing Remarks 10 mins Ningyu Zhang

Accepted Papers

  • Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph,
    Guangyi Liu, Yongqi Zhang, Yong Li, Quanming Yao
  • Technical Domain Question Answering With Large Language Models,
    Jihee Kim, Subeen Park, Hakyung Lee, YongTaek Lim, Hyo-won Suh, Kyungwoo Song
  • InLegalLLaMA: Indian Legal Knowledge Enhanced Large Language Models,
    Sudipto Ghosh (University of Delhi); Devanshu Verma (University of Delhi); Balaji Ganesan (IBM Research)*; Purnima Bindal (University of Delhi); Vikas Kumar (University of Delhi); Vasudha Bhatnagar (Department of Computer Science, University of Delhi)
  • Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph,
    Menghan Wang (eBay)*; Yuchen Guo (eBay Inc.); Duanfeng Zhang (ebay); Jianian Jin (ebay); Minnie Li (ebay); Dan Schonfeld (eBay); Shawn Zhou (eBay)
  • Identification of LLM-Generated Text via Knowledge Potentiality,
    Zhenhua Wang (Renmin University of China); Guang Xu (Renmin University of China); Ming Ren (Renmin University of China)*

  • Identification of fundamental parameters of E-marketing content awareness with artificial intelligence and natural language processing based on deep learning.,
    Pascal Muam Mah (AGH University of Science and Technology Krakow )*
  • Cognitive Mirage: A Review of Hallucinations in Large Language Models,
    Hongbin Ye (Zhejiang Lab)*; Tong Liu (Zhejiang Lab); Aijia Zhang (Zhejiang Lab); Wei Hua (Zhejiang Lab); Weiqiang Jia (Zhejiang Lab)
  • Comparative Analysis of National AI Strategies,
    Pascal Muam Mah (AGH University of Science and Technology Krakow )*
  • Fast and Continual Knowledge Graph Embedding via Incremental LoRA,
    Jiajun Liu (Southeast University)*; Wenjun Ke (School of Computer Science and Engineering, Southeast University); Peng Wang (Southeast University); Jiahao Wang (Southeast University); Jinhua Gao (Institute of Computing Technology, Chinese Academy of Scien
  • Efficient Tuning and Inference for Large Language Models on Textual Graphs,
    Yun Zhu (Zhejiang University)*; Yaoke Wang (Zhejiang University); Haizhou Shi (Rutgers University); Siliang Tang (Zhejiang University)
  • Information Law-Enhanced Prompting for LLM Entity Extraction,
    Zhenhua Wang (Renmin University of China); Huiru Chen (Renmin University of China); Guang Xu (Renmin University of China); Ming Ren (Renmin University of China)*
  • Augmenting LLM based Patent Summarization with Knowledge Graphs,
    Shoon Lei Phyu (Tokyo International University)*; Murataly Uchkempirov (Tokyo International University ); Mayesha Proma (Tokyo International University); Parag Kulkarni (Tokyo International University)
  • $R^3$-NL2GQL: A Hybrid Models Approach for Accuracy Enhancement and Alignment Optimization,
    Yuhang Zhou (Fudan University)*; He Yu (fudan university); siyu tian (Fudan university); Guangnan Ye (Fudan University)
  • Structured knowledge injection and reasoning prompting for compliance on-chain asset analysis,
    hao tan (ZJU)*; Shuangzhou Yan (Zhejiang University); Hongxin Zhang (Zhejiang University); Zhuo Li (State Street Technology (Zhejiang) Ltd.)
  • Knowledge Base-enhanced Multilingual Relation Extraction with Large Language Models,
    Tong Chen (Xi'an Jiaotong-Liverpool University)*; Procheta Sen (University of Liverpool); Zimu Wang (Xi'an Jiaotong-Liverpool University); Zhengyong Jiang (Xi'an Jiaotong-Liverpool University); Jionglong Su (Xi'an Jiaotong-Liverpool University)
  • Designing a Language-Model-Based Chatbot that Considers User¡¯s Personality Profile and Emotions To Support Caregivers of People With Dementia,
    Yeganeh Nasiri (Brigham Young University)*; Nancy Fulda (Brigham Young University)
  • Pre-trained Model Enhanced Contrastive Knowledge Graph Completion,
    Lin Wang (Shanghai International Studies University); Yuan Wang (Ludong University); Wuying Liu (Ludong University)*
  • LLM-Driven Knowledge Enhancement for Securities Index Prediction,
    Zaiyuan Di (Tongji University); Jianting Chen (Tongji university); Yunxiao Yang (Tongji University); Ling Ding (Tongji University); Yang Xiang (Tongji University)*
  • Distilling Event Sequence Knowledge From Large Language Models,
    Somin Wadhwa (Northeastern University)*; Oktie Hassanzadeh (IBM Research); Debarun Bhattacharjya (IBM Research); Ken Barker (IBM Research); Jian Ni (IBM Research AI)
  • Improved KG-RAG to Address Complaints-LLMs Hallucinations,
    Jiaju Kang (School of Computer Science and Technology, Shandong Jianzhu University)*; Kangsong Yuan (School of Computer Science and Technology, Shandong Jianzhu University); Guibing Liu (School of Computer Science and Technology, Shandong Jianzhu Universi
  • Efficient and Accurate Memorable Conversation Model using DPO based on sLLM,
    Youngkyung Seo (KT)*; Yoonseok Heo (KT); Junseok Koh (KT); Duseong Chang (KT)
  • SALMON: Syntactically Analysed and LLM Optimised Natural Language Text for Triple Extraction and Linking,
    Muhammad Salman (The Australian National University)*; Armin Haller (ANU); Sergio Jos¨¦ Rodr¨ªguez M¨¦ndez (The Australian National University)
  • Synergizing Large Language Model Reasoning with Talent Knowledge Graph to Facilitate Scientific Teaming,
    Yikun Han (University of Michigan)*; Jiawei Xu (UT Austin); Zhandos Sembay (UAB); Pamela H Foster (University of Alabama); Jake Chen (UAB); Ying Ding (University of Texas at Austin)
  • An LLM-SPARQL Hybrid Framework for Named Entity Linking and Disambiguation to Wikidata,
    Muhammad Salman (The Australian National University)*; Haoting Chen (The Australian National University); Sergio Jos¨¦ Rodr¨ªguez M¨¦ndez (The Australian National University); Armin Haller (ANU)
  • Prompt-enhanced Large Language Models for Automated Construction of circRNA-themed Hyper-relational Knowledge Graph,
    Yingjie Xiao (Sichuan University)*; Lei Duan (Sichuan University)
  • Leveraging LLM-Constructed Graphs for Effective Goal-Driven Storytelling,
    Taewoo Yoo (sungkyunkwan university)*; Yun-Gyung Cheong (SKKU)
  • C2SDI: Conditional Score-based Diffusion Models with Classifier-free Guidance,
    Joonseong Kang (Yonsei university)*; Seonggyun Lee (Yonsei University); JEYOON YEOM (Yonsei University); Kyungwoo Song (Yonsei University)
  • H-Consistency Bounded Critic for Enhancing Retrieval-Augmented Generation,
    Seonggyun Lee (Yonsei University)*; Hoyoon Byun (Yonsei University); Kyungwoo Song (Yonsei University)

Organization

Workshop Organizers

Steering Committee

Huajun Chen

Zhejiang University

Guilin Qi

Southeast University

Haofen Wang

Tongji University

Program Chairs

Ningyu Zhang

Zhejiang University

Tianxing Wu

Southeast University

Meng Wang

Tongji University

Contact us

Email us at zhangningyu@zju.edu.cn

Participate

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