3rd Workshop on Knowledge Graphs and Big Data

In Conjunction with IEEE Big Data 2023

SCOPE and OBJECTIVES


Knowledge graphs represent world knowledge in entities linked through relationships enabling effective navigation and pattern discovery. Many enterprises have built large-scale knowledge graphs that drive their products. Moreover, simulations and advanced experiments in scientific research have also generated unprecedented big data. Data-driven machine learning methods have shown great potential to accelerate scientific discovery. However, significant amounts of the big data remain underutilized due to data isolation, distribution, and heterogeneity. Applying knowledge graph techniques for extraction, integration, and discovery in big scientific and experimental data is a promising direction.

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


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
  • 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. 25, 2023: Due date for workshop papers submission
  • Nov. 8, 2023: Notification of paper acceptance to authors
  • Nov. 22, 2023: Camera-ready of accepted papers
  • Dec. 15-18, 2023: 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 2023 is required for at least one of the authors for participating in the workshop.

Workshop Organizers


  • Yuan An, Drexel University, ya45@drexel.edu
  • Jane Greenberg, Drexel University, jg3243@drexel.edu
  • Yuan Ling, Amazon.com, ericalingyuan@gmail.com
  • Alex Kalinowski, Drexel University, ajk437@drexel.edu


Program Committee


  • Daniel Korn, University of North Carolina, United States
  • David Cameron, University of Oslo, Norway
  • Edson Lucas, IPRJ/UERJ Polytechnic Institute, Brazili
  • Hongjian Yang, North Carolina State University, United States
  • Kara Schatz, North Carolina State University, United States
  • Mahdi Abdelguerfi, Cannizaro-Livingston Gulf States Center for Environmental Informatics, United States
  • Min Zhang, Huawei, China
  • Paolo Manghi, Istituto di Scienza e Tecnologie dell'Informazione (ISTI) of Consiglio Nazionale delle Ricerche (CNR), Italy
  • Paulo Alencar, University of Waterloo, Canada
  • Rada Chirkova, North Carolina State University, United States
  • Ted Holmberg, University of New Orleans, United States
 

Accepted Papers


  • "STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies," Edward (Ted) Holmberg, Mahdi Abdelguerfi, and Elias Ioup
  • "BUILD-KG: Integrating Heterogeneous Data Into Analytics-Enabling Knowledge Graphs," Kara Schatz, Pei-Yu Hou, Alexey Gulyuk, Yara Yingling, and Rada Chirkova
  • "Knowledge Prompt for Whisper: An ASR Entity Correction Approach with Knowledge Base," Min Zhang, Xiaosong Qiao, Yanqing Zhao, Chang Su, Yinglu Li, Yuang Li, Song Peng, Shimin Tao, and Hao Yang
  • "Supporting Practical URI Mappings in Virtual Knowledge Graph-based Relational Data Integration," Shogo Sato, Tadashi Masuda, and Toshiyuki Amagasa
  • "Knowledge Graphs for Competency-Based Education," Andrea Linxen, Florian Endel, Simone Opel, and Christian Beecks
  • "Leveraging Knowledge Graphs for Matching Heterogeneous Entities and Explanation," Sahar Ghassabi, Behshid Behkamal, and Mostafa Milani
  • "Ontology-Based Generation of Data Platform Assets," Vincenzo de Leo, Gianni Fenu, David Greco, Nicolo' Bidotti, Paolo Platter, Enrico Motta, Andrea Nuzzolese, Francesco Osborne, and Diego Reforgiato
  • "Characterizing Evolutionary Trends in Temporal Knowledge Graphs with Linear Temporal Logic," Valeria Fionda and Giuseppe Pirró
  • "GCN-based Explainable Recommendation using a Knowledge Graph and a Language Model," Jeongbin Lee, Kunyoung Kim, Mye Sohn, and Jongmo Kim
  • "Knowledge Graphs in Spatial-Temporal Cluster Evolution Analysis," Ivens Portugal, Paulo Alencar, and Donald Cowan
  • "Force-directed graph embedding with hops distance," Hamidreza Lotfalizadeh and Mohammad Al Hasan
  • "IoT-Reg: A Comprehensive Knowledge Graph for Real-Time IoT Data Privacy Compliance," Kelvin Echenim and Karuna Joshi


Agenda

3rd Workshop on Knowledge Graphs and Big Data

In Conjunction with IEEE Big Data 2023

Friday, Dec. 15, 2023

Workshop Co-Chairs: Yuan An, Jane Greenberg, Yuan Ling, Alex Kalinowski

Time (Central European Standard Time GMT+1)

Title

Authors

9:00-9:15am

STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies

Edward (Ted) Holmberg, Mahdi Abdelguerfi, and Elias Ioup

9:15-9:30am

BUILD-KG: Integrating Heterogeneous Data Into Analytics-Enabling Knowledge Graphs

Kara Schatz, Pei-Yu Hou, Alexey Gulyuk, Yara Yingling, and Rada Chirkova

9:30-9:45am

Knowledge Prompt for Whisper: An ASR Entity Correction Approach with Knowledge Base

Min Zhang, Xiaosong Qiao, Yanqing Zhao, Chang Su, Yinglu Li, Yuang Li, Song Peng, Shimin Tao, and Hao Yang

9:45-10:00am

Supporting Practical URI Mappings in Virtual Knowledge Graph-based Relational Data Integration

Shogo Sato, Tadashi Masuda, and Toshiyuki Amagasa

10:00-10:15am

Coffee Break

10:15-10:30am

Knowledge Graphs for Competency-Based Education

Andrea Linxen, Florian Endel, Simone Opel, and Christian Beecks

10:30-10:45am

Leveraging Knowledge Graphs for Matching Heterogeneous Entities and Explanation

Sahar Ghassabi, Behshid Behkamal, and Mostafa Milani

10:45-11:00am

Ontology-Based Generation of Data Platform Assets

Vincenzo de Leo, Gianni Fenu, David Greco, Nicolo' Bidotti, Paolo Platter, Enrico Motta, Andrea Nuzzolese, Francesco Osborne, and Diego Reforgiato

11:00-11:15am

Characterizing Evolutionary Trends in Temporal Knowledge Graphs with Linear Temporal Logic

Valeria Fionda and Giuseppe Pirró

11:15-11:30am

GCN-based Explainable Recommendation using a Knowledge Graph and a Language Model

Jeongbin Lee, Kunyoung Kim, Mye Sohn, and Jongmo Kim

11:30-11:45am

Knowledge Graphs in Spatial-Temporal Cluster Evolution Analysis

Ivens Portugal, Paulo Alencar, and Donald Cowan

11:45-12:00pm

Force-directed graph embedding with hops distance

Hamidreza Lotfalizadeh and Mohammad Al Hasan

12:00pm-12:15pm

IoT-Reg: A Comprehensive Knowledge Graph for Real-Time IoT Data Privacy Compliance

Kelvin Echenim and Karuna Joshi