4th Workshop on Knowledge Graphs and Big Data

In Conjunction with IEEE Big Data 2024

SCOPE and OBJECTIVES


Knowledge graphs have become essential tools for representing world knowledge through interconnected entities and relationships, enabling efficient navigation and pattern discovery. Many enterprises now harness large-scale knowledge graphs to enhance products and services. Similarly, in scientific research, the surge in big data from simulations and experiments has opened new avenues for discovery. Yet, much of this data remains untapped due to challenges like data isolation and heterogeneity. Knowledge graph techniques offer a promising solution by facilitating the extraction, integration, and discovery of insights within these vast datasets.

This workshop provides a platform for knowledge graph researchers and practitioner in both business and scientific domains to exchange research ideas and solutions related to knowledge graph representation, miming, reasoning, and applications in big data settings.


Call for Papers


Accepted papers will be published in the IEEE BigData Conference Proceedings.

The following is a list of topics (but not limited) related this workshop:

  • Knowledge graphs for big scientific and experimental data
  • Big knowledge graph representation and modeling
  • Constructing knowledge graphs from structured and unstructured data
  • Big knowledge graph embeddings
  • Knowledge graphs and large language models (LLMs)
  • Link prediction
  • Knowledge graph completion
  • Natural language processing and knowledge graph
  • Semantic Web, ontology and knowledge graph
  • Knowledge graph for recommender systems
  • Scalable knowledge graph reasoning and inference
  • Knowledge graph for big data processing
  • Knowledge graph applications in business, biomedical, healthcare, etc.
  • Knowledge graph visualization and human interaction
  • Knowledge graph for explainable AI
  • Knowledge graph alignment
  • Graph neural networks and big knowledge graphs
  • Scalable knowledge graph storage and query processing
  • Record linkage using knowledge graphs

Important Dates


  • Oct. 27, 2024: Due date for workshop papers submission
  • Nov. 10, 2024: Notification of paper acceptance to authors
  • Nov. 17, 2024: Camera-ready of accepted papers
  • Dec. 15-18, 2024: Workshop


Paper Submission


submission link: Click Here or you can find the workshop submission link from the list of workshops Here.

Note:

  1. This workshop accepts both long papers (up to 10 pages) and short/position papers (2-4 pages).
  2. Papers should be formatted to IEEE Computer Society Proceedings Manuscript.
  3. Formatting Guidelines ( https://www.ieee.org/conferences/publishing/templates.html ).
  4. Papers should be in the IEEE 2-column format.
  5. Full registration of IEEE BigData 2024 is required for at least one of the authors for participating in the workshop.
  6. Accepted papers will be published in the IEEE BigData Conference Proceedings.

Workshop Organizers


  • Yuan An, Drexel University, ya45@drexel.edu
  • Jane Greenberg, Drexel University, jg3243@drexel.edu
  • Alex Kalinowski, Drexel University, ajk437@drexel.edu


Program Committee


  • Alexander Tropsha, University of North Carolina, United States
  • Aviv Segev, University of South Alabama, United States
  • Brian Smith, Boston College, USA
  • Christian Beecks, University of Hagen, Germany
  • Daniel Korn, University of North Carolina, United States
  • diego Reforgiato, University of Cagliari, Italy
  • Edson Lucas, IPRJ/UERJ Polytechnic Institute, Brazil
  • Hamidreza Lotfalizadeh, Purdue University, USA
  • Ivens Portugal, University of Waterloo, Canada
  • Jongmo Kim, King’s College London, United Kingdom
  • Kara Schatz, Xavier University, USA
  • Kelvin Echenim, University of Maryland Baltimore County, United States
  • Mahdi Abdelguerfi, Cannizaro-Livingston Gulf States Center for Environmental Informatics, United States
  • Michelle Rogers, Drexel University, USA
  • Min Zhang, Huawei, China
  • Mostafa Milani, Western University, Canada
  • Paolo Manghi, Istituto di Scienza e Tecnologie dell'Informazione (ISTI) of Consiglio Nazionale delle Ricerche (CNR), Italy
  • Paulo Alencar, University of Waterloo, Canada
  • Pei-Yu Hou, National Chengchi University, Taiwan
  • Rada Chirkova, North Carolina State University, USA
  • Ted Holmberg, University of New Orleans, United States
  • Toshiyuki Amagasa, University of Tsukuba, Japan

Accepted Papers


TBA...


Agenda

TBA...