LLM+KG

LLM+KG:

Large Language Models (LLMs), e.g., ChatGPT and LLaMA are revolutionizing the fields of artificial intelligence and natural language processing (NLP). Recent LLMs browse Web knowledge and learn from external sources, warranting the coupling of knowledge graphs (KGs) and LLMs. The possibility of bridging KGs with LLMs has attracted increasing interest in the area of knowledge engineering. On one hand, LLMs can be enhanced with KGs to provide answers with more contextualized facts. On the other hand, downstream tasks, e.g., KG curation, embedding, and search can also benefit by adopting LLMs. It remains an interesting direction to explore effective interactions between LLMs and KGs, where many recent advances come from deep learning, information retrieval, NLP, and computer vision domains. The workshop, titled “LLM+KG: Data Management Opportunities in Unifying Large Language Models + Knowledge Graphs”, is targeted for data management researchers, aiming to discuss interesting opportunities such as data cleaning, modeling, designing of algorithms and systems, scalability, fairness, privacy, usability, explainability, and etc.

We solicit unpublished papers discussing issues and successes under the broad category of LLM-enhanced KGs, KG-enhanced LLMs, and unifying LLMs + KGs in the following areas (and beyond):

• KG-enhanced Pre-training of LLMs

• KG-enhanced Fine-tuning of LLMs

• KG-enhanced Inference of LLMs

• KG-enhanced Validation and Explainability of LLMs

• LLM-enhanced KG Creation

• LLM-enhanced KG Completion

• LLM-enhanced KG Embedding

• LLM-enhanced KG Querying

• LLM-enhanced KG Analytics

• LLM-enhanced Domain-specific KG Applications

Additionally, the paper must have a clear data management focus, e.g., discussing data management solution(s) such as (but not limited to):

• Data and Input Modeling for LLMs+KGs

• Data Cleaning, Integration, and Augmentation with LLMs+KGs

• Multi-modal Data Management with LLMs+KGs

• Vector Data Management for LLMs+KGs

• Accuracy and Consistency of LLMs+KGs

• Efficiency and Scalability of LLMs+KGs

• Bias and Fairness with LLMs+KGs

• Explainability and Provenance of LLMs+KGs

• Usability of LLMs+KGs

• Security and Privacy for LLMs+KGs

• Optimizing KG Databases and Systems with LLMs

• Empirical Benchmark and Ground Truth in Emerging Applications with LLMs+KGs

Submission Details:

We solicit and select five types of papers

• Survey Papers: these papers survey the related work in specific sub-areas and lay out the agenda for future work.

• Regular Research Papers: these are research papers with different flavors including foundations, algorithms, systems, information system architectures, experimental benchmarking, and applications. Papers with new/ late-breaking results are also welcome, which report the newest preliminary results about the most promising problems in the field.

• Vision Papers: these papers are devoted to discussing problems that we face currently and anticipate for the future.

• Demonstration Papers: these are software demonstration proposals, accompanied by short papers. The paper must describe the demonstrated system, user interface, options for user interactions, the system setup, and state the novelty and significance. We encourage providing the online link of a demonstration video, which is accessible by the reviewers.

• Extended Abstract Papers: if your research article has been accepted elsewhere, you are eligible to submit your work in the form of an extended abstract under the short paper category, while citing your previously published article. We encourage you to rephrase when needed or possible to avoid substantial verbatim text overlapping with past accepted/ published materials.

For survey, vision, demonstration, and extended abstract papers, you require to add [Survey], [Vision], [Demo], [Extended Abstract], respectively, next to the paper title, in both CMT submission form and also in the submitted paper pdf. We welcome the papers that fall under short papers of at most 9 pages and long papers up to 18 pages, including bibliography. Submissions must adhere to CEUR-WS formatting guidelines with 1-column style available at: http://ceur-ws.org/Vol-XXX/CEURART.zip. An Overleaf page for LaTeX users is available as template at: https://www.overleaf.com/read/xztwvxtwbzrn#ac9ca2. All submissions must be submitted in PDF through: https://cmt3.research.microsoft.com/LLMKG2024/. Submissions will be reviewed in a single-blind manner, and all author names and affiliations should be included. Papers that do not follow the guidelines or are not within the scope of relevant topics will be desk rejected. We also expect that publications from DB venues, e.g., SIGMOD/VLDB/ICDE/EDBT etc. are cited.

Submissions will be reviewed by at least three members of the Program Committee. All accepted papers will be published online via CEUR-WS. The workshop will be in-person and at least one author of each accepted paper is required to register (VLDB registration link: https://vldb.org/2024/?info-registration). Best papers will be invited to submit extensional versions to the special issue: Neuro-Symbolic Intelligence: Large Language Model Enabled Knowledge Engineering in the World Wide Web Journal, and the Data Intelligence Journal.

Accepted Paper List:

OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Ningyu Zhang, Zekun Xi, Yujie Luo, Peng Wang, Bozhong Tian, Yunzhi Yao, Jintian Zhang, Shumin Deng, Mengshu sun, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen

Knowledge Graph Efficient Construction: Embedding Chain-of-Thought into LLMs
Jixuan Nie, Xia Hou, Wenfeng Song, Xuan Wang, Xingliang Jin, Xinyu Zhang, ShuoZhe Zhang, Jiaqi Shi

Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Yusuf Abdulle, Emily Groves, Minhong Wang, Holger Kunz, Jason Hoelscher-Obermaier, Ronin Wu, Honghan Wu

Leveraging LLMs Few-shot Learning to Improve Instruction-driven Knowledge Graph Construction
Yongli Mou, Li Liu, Sulayman Sowe, Diego Collarana, Stefan Decker

Research Trends for the Interplay between Large Language Models and Knowledge Graphs
Hanieh Khorashadizadeh

Enhancing Large Language Models with Multimodality and Knowledge Graphs for Hallucination-free Open-set Object Recognition
Xinfu Liu, Yirui Wu, Yuting Zhou, Junyang Chen, Huan Wang, Ye Liu, Shaohua Wan

SPIREX: Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning
Emanuele Cavalleri, Mauricio Soto-Gomez, Ali Pashaeibarough, Dario Malchiodi, Harry Caufield, Justin Reese, Chris J Mungall, Peter Robinson, Elena Casiraghi, Giorgio Valentini, Marco Mesiti

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
Fali Wang , Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen

From Instructions to ODRL Usage Policies: An Ontology Guided Approach
Daham M. Mustafa, Abhishek Nadgeri, Diego Collarana, Benedikt T. Arnold, Christoph Quix, Christoph Lange, Stefan Decker

Camera-ready Instructions:

This is the first time that VLDB has decided to publish the VLDB workshop papers on vldb.org.
First, format your paper using the template available at: vldb-workshop-style-master.zip. Second, fill in the copyright form available at: https://vldb.org/pvldb/vol13/VLDB_Copyright_License_Form.pdf. Please note that papers not formatted in the given template or without an associated copyright form can't be published on vldb.org. Third, please send one email to tianxingwu@seu.edu.cn (and -cc to both arijitk@cs.aau.dk and jasonxchen@tencent.com), with title “LLM+KG Camera Ready-PaperID”, attaching your camera-ready paper pdf and filled-in copyright pdf. Name them as LLMKG-PaperID.pdf and LLMKG-PaperID-Copyright.pdf, respectively. The PaperID is same as your numeric paperID shown at the CMT submission site. For example, if your PaperID is 2 at the CMT submission site, you must name your pdf files as LLMKG-2.pdf and LLMKG-2-Copyright.pdf, respectively, and the email title: LLM+KG Camera Ready-2. Please send this email latest by August 9, 17:00 PST. Note that this is a hard deadline, and if not received by this deadline, the paper cannot be published on vldb.org.