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, 2024Nov. 4, 2024: Due date for workshop papers submission
  • Nov. 10, 2024Nov.14, 2004: Notification of paper acceptance to authors
  • Nov. 23, 2024: Camera-ready of accepted papers
  • Dec. 15, 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


  1. Antor Mahmud and Renata Dividino. Federated Learning on Knowledge Graph Embeddings via Contrastive Alignment
  2. Nahed Abu Zaid, Kara Schatz, Kimberly Bourne, Darrell Harry, Christine Hendren, Anna-Maria Marshall, Khara Grieger, Jacob Jones, Alexey V. Gulyuk, Yaroslava G. Yingling, and Rada Chirkova. INTEGRATE-KG: A Workflow For Unifying Heterogeneous Data Driven by Shared Languages
  3. Edward Kim, Manil Shrestha, Richard Foty, Tom DeLay, and Vicki Seyfert-Margolis. Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search
  4. Zhuocheng Mei, Kara Schatz, Nahed Abu Zaid, and Rada Chirkova. Semantics-Aware Path Ranking On Information Extracted from Knowledge Graphs
  5. Tong Liu and Hadi Meidani. Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models
  6. Tangrui Li, Jun Zhou, and Hongzheng Wang. Aligning Knowledge Graphs Provided by Humans and Generated by Neural Networks
  7. Ted Edward Holmberg, Elias Ioup, and Mahdi Abdelguerfi. Knowledge Graph-Based Multi-Agent Path Planning in Dynamic Environments using WAITR
  8. Joseph Cottam, Patrick Mackey, Sumit Purohit, and George Chin. Contradictory Ambiguous Revocable Assertion Tracker (CARAT) Encoding
  9. Subbareddy Batreddy, Giridhar Pamisetty, Manish Kothuri, Priya Verma, and Sobhan Babu Ch. Adaptive Neighborhood Sampling and KAN-Based Edge Features for Enhanced Fraud Detection
  10. Alexandria Barghi. Accelerating Graph Query Languages for Machine Learning and Retrieval Augmented Generation
  11. Vladimir Korolev and Anupam Joshi. Crystalia: Flexible and Efficient Method for Large Dataset Lineage Tracking
  12. Duy Ho, Udiptaman Das, Regina Ho, and Yugyung Lee. Leveraging Multi-Agent Systems and Large Language Models for Diabetes Knowledge Graphs
  13. Diego Russo, Gian Marco Orlando, Antonio Romano, Giuseppe Riccio, Valerio La Gatta, Marco Postiglione, and Vincenzo Moscato. Scaling LLM-Based Knowledge Graph Generation: A Case Study of Italian Geopolitical News
  14. Sumit Purohit, George Chin, Patrick Mackey, and Joseph Cottam. GraphAide: Advanced Graph-Assisted Query and Reasoning System
  15. Subhankar Chattoraj and Karuna Pande Joshi. MedReg-KG: KnowledgeGraph for Streamlining Medical Device Regulatory Compliance
  16. Gabriel Santos, Rita Julia, and Marcelo Nascimento. K-GBS3FCM - KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means
  17. Yuan An, Samarth Kolanupaka, Jacob An, Matthew Ma, Unnat Chhatwal, Alex Kalinowski, Michelle Rogers, and Brian K. Smith. Is the Lecture Engaging? Lecture Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System
  18. Yuan Ma, Michelle Dea, Lilian Cao, and Munehiro Fukuda. Toward Implementing an Agent-based Distributed Graph Database System
  19. Artem Sukhobokov, Artem Vetoshkin, Alexandra Mironova, Anna Zenger, and Vitaly Baklikov. Archigraphs As A Promising Concept For The Development Of Data And Knowledge Storage Systems


Agenda

4th  Workshop on Knowledge Graphs and Big Data

Sunday, Dec. 15, 2024

Location: Columbia-C

Hyatt Regency Washington on Capitol Hill

400 New Jersey Avenue, NW

Washington, D.C. 20001 United States

Workshop Co-Chairs: Yuan An and Jane Greenberg

Time (Eastern Standard Time GMT-5)

Title

Authors

9:00-9:15

Federated Learning on Knowledge Graph Embeddings via Contrastive Alignment

Antor Mahmud and Renata Dividino

9:15-9:30

INTEGRATE-KG: A Workflow For Unifying Heterogeneous Data Driven by Shared Languages

Nahed Abu Zaid, Kara Schatz, Kimberly Bourne, Darrell Harry, Christine Hendren, Anna-Maria Marshall, Khara Grieger, Jacob Jones, Alexey V. Gulyuk, Yaroslava G. Yingling, and Rada Chirkova

9:30:9:45

Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search

Edward Kim, Manil Shrestha, Richard Foty, Tom DeLay, and Vicki Seyfert-Margolis

9:45-10:00

Semantics-Aware Path Ranking On Information Extracted from Knowledge Graphs

Zhuocheng Mei, Kara Schatz, Nahed Abu Zaid, and Rada Chirkova

10:00-10:30

Coffee Break

10:30-10:45

Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models

Tong Liu and Hadi Meidani

10:45-11:00

Aligning Knowledge Graphs Provided by Humans and Generated by Neural Networks

Tangrui Li, Jun Zhou, and Hongzheng Wang

11:00-11:15

Knowledge Graph-Based Multi-Agent Path Planning in Dynamic Environments using WAITR

Ted Edward Holmberg, Elias Ioup, and Mahdi Abdelguerfi

11:15-11:30

Contradictory Ambiguous Revocable Assertion Tracker (CARAT) Encoding

Joseph Cottam, Patrick Mackey, Sumit Purohit, and George Chin

11:30-11:45

Adaptive Neighborhood Sampling and KAN-Based Edge Features for Enhanced Fraud Detection

Subbareddy Batreddy, Giridhar Pamisetty, Manish Kothuri, Priya Verma, and Sobhan Babu Ch

11:45-12:00

Accelerating Graph Query Languages for Machine Learning and Retrieval Augmented Generation

Alexandria Barghi

12:00-12:15

Crystalia: Flexible and Efficient Method for Large Dataset Lineage Tracking

Vladimir Korolev and Anupam Joshi

12:15-12:30

Scaling LLM-Based Knowledge Graph Generation: A Case Study of Italian Geopolitical News

Diego Russo, Gian Marco Orlando, Antonio Romano, Giuseppe Riccio, Valerio La Gatta, Marco Postiglione, and Vincenzo Moscato

12:30-14:00

Lunch (on your own)

14:00-14:15

Leveraging Multi-Agent Systems and Large Language Models for Diabetes Knowledge Graphs

Duy Ho, Udiptaman Das, Regina Ho, and Yugyung Lee

14:15-14:30

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Sumit Purohit, George Chin, Patrick Mackey, and Joseph Cottam

14:30-14:45

MedReg-KG: KnowledgeGraph for Streamlining Medical Device Regulatory Compliance

Subhankar Chattoraj and Karuna Pande Joshi

14:45-15:00

K-GBS3FCM - KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means

Gabriel Santos, Rita Julia, and Marcelo Nascimento

15:00-15:15

Is the Lecture Engaging? Lecture Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System

Yuan An, Samarth Kolanupaka, Jacob An, Matthew Ma, Unnat Chhatwal, Alex Kalinowski, Michelle Rogers, and Brian K. Smith

15:15-15:30

Toward Implementing an Agent-based Distributed Graph Database System

Yuan Ma, Michelle Dea, Lilian Cao, and Munehiro Fukuda

15:30-15:45

Archigraphs As A Promising Concept For The Development Of Data And Knowledge Storage Systems

Artem Sukhobokov, Artem Vetoshkin, Alexandra Mironova, Anna Zenger, and Vitaly Baklikov

15:45-16:00

 

 

16:00-16:30

Coffee Break