5th Workshop on Knowledge Graphs and Big Data (Virtual)

In Conjunction with IEEE Big Data 2025

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 (Extended Deadlines)


  • Oct. 27 Nov. 3, 2025: Due date for workshop papers submission
  • Nov. 10 Nov. 17, 2025: Notification of paper acceptance to authors
  • Nov. 23, 2025: Camera-ready of accepted papers
  • Dec. 8-11, 2025: Workshop (Virtual)


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 2025 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
  • Edward Kim, Drexel University, ek826@drexel.edu


Program Committee


  1. Mahdi Abdelguerfi, Cannizaro-Livingston Gulf States Center for Environmental Informatics, USA
  2. Nahed Abu Zaid, North Carolina State University, USA
  3. Toshiyuki Amagasa, University of Tsukuba, Japan
  4. Christian Beecks, University of Hagen, Germany
  5. Subhankar Chattoraj, University of Maryland Baltimore County, USA
  6. Rada Chirkova, North Carolina State University, USA
  7. Tejaswani Dash, Capital Group, USA
  8. Kelvin Echenim, University of Maryland Baltimore County, USA
  9. Munehiro Fukuda, Computing and Software Systems, University of Washington Bothell, USA
  10. Duy Ho, California State University Fullerton, USA
  11. Ted Holmberg, University of New Orleans, USA
  12. Pei-Yu Hou, National Chengchi University, Taiwan
  13. Daniel Korn, UNC Chapel Hill, USA
  14. Tong Liu, University of Illinois at Urbana-Champaign, USA
  15. Edson Lucas, IPRJ/UERJ Polytechnic Institute, Brazil
  16. Patrick Mackey, Pacific Northwest National Lab, USA
  17. Vincenzo Moscato, University of Naples Federico II, Italy
  18. Giridhar Pamisetty, IIT Hyderabad, India
  19. Ivens Portugal, University of Waterloo, Canada
  20. diego Reforgiato, University of Cagliari, Italy
  21. Kara Schatz, Xavier University, USA
  22. Aviv Segev, University of South Alabama, USA
  23. Artem Sukhobokov, SAP America, Inc., USA
  24. Min Zhang, Huawei, China

Accepted Papers


  1. Hieu Duong, Eugene Levin, Todd Gary, and Long Nguyen. CaST: Causal Discovery via Spatio-Temporal Graphs in Disaster Tweets
  2. Bara Diop, Cheikh Talibouya Diop, and Lamine Diop. One-Class Outlier Detection of Label-Induced Ambiguity in Knowledge Graphs
  3. Moritz Busch, Giuliana Defilippis, Philipp Weiß, Christian Beecks, Stefan Decker, and Diego Collarana. GRAFT - Graph Retrieval Augmented Generation Fine-Tuning Approach
  4. Manil Shrestha and Edward Kim. Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search
  5. Shenghao Huang, Yoshihide Kato, and Shigeki Matsubara. A commonsense knowledge graph for representing exceptions concerning extrinsic factors
  6. Kehan Hu. Learning Recommendation under an Identical-Pool Protocol: Top-K Ranking on Skill Graphs with Fair, Reproducible Evaluation
  7. Alexander Graß, Jonathan Lehmkuhl, Diego Collarana Vargas, Stefan Decker, and Christian Beecks. Code2Onto: Multi-Agent System for Code-Driven Ontology Population
  8. Nhan Vo, Nhat Truong, Tuan Bui, An Nguyen, Khang Huynh, Tho Quan, and Thang Bui. Enhancing Link Prediction with Attentive-HINGE: A Vietnamese Case Study in Educational Knowledge Graphs
  9. Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy, and Vaibhav Gandhi. HyperComplEx: Adaptive Multi-Space Knowledge Graph Embeddings
  10. Xinyi Fang, Kyumin Lee, and Yichuan Li. LLM Profiling and Fine-Tuning with Limited Neighbor Information for Node Classification on Text-Attributed Graphs
  11. DORSAF SELLAMI, Wissem Inoubli, Imed Riadh Farah, and Sabeur Aridhi. SA-KGP: A Semantic-Aware Partitioning Method for Scalable Knowledge Graph Embedding
  12. Nahed Abu Zaid, Kara Schatz, Deepak Sai Pendyala, Alexey Gulyuk, Yaroslava Yingling, and Rada Chirkova. RL-CURATE-KG: Multi-Agent Reinforcement Learning for Scalable Knowledge Graph Curation
  13. Yi-Mo Ho, Wei-Pin Ku, and Pei-Yu Hou. LiteKG: A Lightweight LLM-Assisted Framework for Domain-Specific Knowledge Graph Construction
  14. Maryam Mubarak, Hanqi Chen, Nahed Abu Zaid, Kara Schatz, and Rada Chirkova. Bridging Semantic Gaps in Federated Knowledge Graphs with Context-Enriched Synonym Detection
  15. Asma Houimli, Zaineb Gabsi, and Sabri Skhiri. Evaluation of GraphRAG Strategies for Efficient Information Retrieval
  16. Edward Holmberg, Elias Ioup, and Mahdi Abdelguerfi. A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics
  17. Yuan An, Ruhma Hashmi, Michelle Rogers, Jane Greenberg, and Brian K. Smith. Rate-Distortion Guided Knowledge Graph Construction from Lecture Notes Using Gromov-Wasserstein Optimal Transport
  18. Bedirhan Gergin and Charalampos Chelmis. Retrieval-Reranker: A Two-Stage Pipeline for Knowledge Graph Completion
  19. Nahed Abu Zaid, Kara Schatz, Zhuocheng Mei, and Rada Chirkova. AMSP-KG: Automated Mapping of Sentences to Paths in Knowledge Graphs
  20. Jin Zhang, Wengen LI, Huiling Liu, Yuan Zhao, Jianing Chen, Jian Liu, and Chengkun Wu. DTRAG: Triple-Fusion Retrieval-Augmented Generation Leveraging a Knowledge Graph for Answering Diabetes Questions


Agenda (Tentative)

 

5th Workshop on Knowledge Graphs and Big Data

Sunday, Dec. 8, 2025

Location: Virtual (https://drexel.zoom.us/j/81283488336 passcode: 367682)

(IEEE Big Data 2025 Conference Venue:

Galaxy International Convention Center

Address: Galaxy Macau Cotai Macau, Macau SAR, China)

Workshop Co-Chairs: Yuan An, Jane Greenberg, Edward Kim

China Standard Time

Time zone in Macau (GMT+8)

Title

Authors

 

First Half

 

9:00-9:15

CaST: Causal Discovery via Spatio-Temporal Graphs in Disaster Tweets

Hieu Duong, Eugene Levin, Todd Gary, and Long Nguyen

9:15-9:30

One-Class Outlier Detection of Label-Induced Ambiguity in Knowledge Graphs

Bara Diop, Cheikh Talibouya Diop, and Lamine Diop

9:30:9:45

GRAFT - Graph Retrieval Augmented Generation Fine-Tuning Approach

Moritz Busch, Giuliana Defilippis, Philipp Weiß, Christian Beecks, Stefan Decker, and Diego Collarana

9:45-10:00

Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search

Manil Shrestha and Edward Kim

10:00-10:15

A commonsense knowledge graph for representing exceptions concerning extrinsic factors

Shenghao Huang, Yoshihide Kato, and Shigeki Matsubara

10:15-10:30

Learning Recommendation under an Identical-Pool Protocol: Top-K Ranking on Skill Graphs with Fair, Reproducible Evaluation

Kehan Hu

10:30-10:45

Code2Onto: Multi-Agent System for Code-Driven Ontology Population

Alexander Graß, Jonathan Lehmkuhl, Diego Collarana Vargas, Stefan Decker, and Christian Beecks

10:45-11:00

Enhancing Link Prediction with Attentive-HINGE: A Vietnamese Case Study in Educational Knowledge Graphs

Nhan Vo, Nhat Truong, Tuan Bui, An Nguyen, Khang Huynh, Tho Quan, and Thang Bui

11:00-11:15

HyperComplEx: Adaptive Multi-Space Knowledge Graph Embeddings

Jugal Gajjar, Kaustik Ranaware, Kamalasankari Subramaniakuppusamy, and Vaibhav Gandhi

11:15-11:30

LLM Profiling and Fine-Tuning with Limited Neighbor Information for Node Classification on Text-Attributed Graphs

Xinyi Fang, Kyumin Lee, and Yichuan Li

 

Second Half

 

11:30-11:45

SA-KGP: A Semantic-Aware Partitioning Method for Scalable Knowledge Graph Embedding

DORSAF SELLAMI, Wissem Inoubli, Imed Riadh Farah, and Sabeur Aridhi

11:45-12:00

RL-CURATE-KG: Multi-Agent Reinforcement Learning for Scalable Knowledge Graph Curation

Nahed Abu Zaid, Kara Schatz, Deepak Sai Pendyala, Alexey Gulyuk, Yaroslava Yingling, and Rada Chirkova

12:00-12:15

LiteKG: A Lightweight LLM-Assisted Framework for Domain-Specific Knowledge Graph Construction

Yi-Mo Ho, Wei-Pin Ku, and Pei-Yu Hou

12:15-12:30

Bridging Semantic Gaps in Federated Knowledge Graphs with Context-Enriched Synonym Detection

Maryam Mubarak, Hanqi Chen, Nahed Abu Zaid, Kara Schatz, and Rada Chirkova

12:30-12:45

Evaluation of GraphRAG Strategies for Efficient Information Retrieval

Asma Houimli, Zaineb Gabsi, and Sabri Skhiri

12:45-13:00

A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics

Edward Holmberg, Elias Ioup, and Mahdi Abdelguerfi

13:00-13:15

Rate-Distortion Guided Knowledge Graph Construction from Lecture Notes Using Gromov-Wasserstein Optimal Transport

Yuan An, Ruhma Hashmi, Michelle Rogers, Jane Greenberg, and Brian K. Smith

13:15-13:30

Retrieval-Reranker: A Two-Stage Pipeline for Knowledge Graph Completion

Bedirhan Gergin and Charalampos Chelmis

13:30-13:45

AMSP-KG: Automated Mapping of Sentences to Paths in Knowledge Graphs

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

13:45-14:00

DTRAG: Triple-Fusion Retrieval-Augmented Generation Leveraging a Knowledge Graph for Answering Diabetes Questions

Jin Zhang, Wengen LI, Huiling Liu, Yuan Zhao, Jianing Chen, Jian Liu, and Chengkun Wu