Workshop on Knowledge Graphs and Big Data

In Conjunction with IEEE Big Data 2021

SCOPE and OBJECTIVES


Knowledge graphs represent world knowledge in multigraph and labeled structures. Entities linked through relationships enable effective navigation and pattern discovery. Knowledge graphs have become an integral part of many AI applications empowered by sophisticated machine learning models including deep neural networks. Vast amount of unstructured data on the Web are analyzed and converted to knowledge graphs for improved machine processing. Moreover, many enterprises are building large-scale knowledge graphs that drive their products.

In past several years, knowledge graph research has attracted an increased attention in both academia and industry. While publicly available knowledge graphs such as FreeBase and YAGO are useful for experimenting with novel ideas, large-scale real-world knowledge graphs start to play a critical role in biomedical, healthcare, and business applications, including drug discovery, fraud detection, item recommendation, search engine, question answering, financial intelligence, image processing, virtual assistant, human computer interaction, and robotics. Big industry-scale knowledge graphs bring out many new research issues.

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


Call for Papers


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

  • Big knowledge graph representation and modeling
  • Constructing knowledge graphs from structured and unstructured data
  • Big knowledge graph embeddings
  • 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 15, 2021 Oct. 22, 2021 (extended): Due date for full workshop papers submission
  • Nov 5, 2021 Nov. 12, 2021 (extended): Notification of paper acceptance to authors
  • Nov 20, 2021: Camera-ready of accepted papers
  • Dec 15-18, 2021: Workshops


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 2021 is required for at least one of the authors for participating in the workshop.

Workshop Organizers


  • Yuan An, Drexel University, ya45@drexel.edu
  • Dejing Dou, University of Oregon, dou@cs.uoregon.edu
  • Yuan Ling, Amazon.com, ericalingyuan@gmail.com
  • Alex Kalinowski, Drexel University, ajk437@drexel.edu


Program Committee


  • Russa Biswas, FIZ Karlsruhe, Germany
  • Zheng Chen, Amazon.com, USA
  • Wanying Ding, JPMorgan, USA
  • Mauro Dragoni, Fondazione Bruno Kessler, Italy
  • Jane Greenberg, Drexel University, USA
  • Sadid Hasan, CVS Health, USA
  • Ritu Khare, IQIVIA, USA
  • Juan Sequeda, Data.world, USA
  • Yanshan Wang, University of Pittsburgh, USA
  • Xintong Zhao, Drexel University

Accepted Papers


TBA


Agenda


TBA