EKG-LLM 2023 : Workshop on Enterprise Knowledge Graphs using Large Language Models

Knowledge graphs can integrate diverse data sources and provide a holistic view to the downstream applications. By virtue of being structured, knowledge graphs offer transparency and interpretability to the search and recommendations applications. Combining Knowledge Graphs with current-day advances in LLMs can create several opportunities.

The EKG-LLM workshop as part of CIKM 2023, would be addressing how Large Language Models (LLMs) can help with the construction and usage of enterprise knowledge graphs. This involves improving all the aspects of EKG workflow using large language models: entity extraction, entity enrichment, EKG construction, querying EKG for search and recommendations, scenario specific EKG, etc. Through this workshop we would like to highlight research issues specific to the integration of the enterprise knowledge graphs with large language models and associated applications.

Topics of interest include but are not limited to, the following:

1. Designing Enterprise Knowledge Graph (EKG)
2. EKG Implementation
3. Scalable extraction of enterprise entities using LLMs
4. Building EKGs for specific domains or applications
5. Natural Language Processing (NLP) algorithms to build EKGs
6. Relationship extraction using large language models
7. Federated graph learning with LLMs
8. Privacy in graph algorithms
9. Privacy preserving graph construction and mining
10. Semantic reasoning based on deep learning on graph
11. Industrial applications of EKGs: banking, financing, retail, healthcare, medicine, etc.
12. Explainable AI based on EKG
13. Use of EKG and LLMs for search and recommendations

Submission Guidelines:

Submissions should be made to the EKG-LLM 2023 Easychair site (https://easychair.org/conferences/?conf=ekgllm2023). Two-column CEUR style template (https://ceur-ws.org/Vol-XXX/) should be followed.
Submissions should be in PDF format with upto 6 pages of content, plus references

Committees:

Program Committee:
Manoj Agarwal (Senior Researcher, Discovery Intelligence, Uber Research)
Manish Bhide (CTO, AI Governance, IBM)
Mukesh Mohania (Professor, CSE, IIIT Delhi)
Prasad Deshpande (Senior Staff Software Engineer, Databricks)
Qi He (Head of AI, Nextdoor)
Ranganath Kondapally (Principal Applied Scientist, Microsoft)
Rushi Bhatt (Partner, ML Systems and Services, Microsoft)
Sauvik Ghosh (Director of AI, LinkedIn)

Workshop Chairs:
Rajeev Gupta (Principal scientist, Microsoft, India): [email protected]
Srinath Srinivasa (Professor and Dean (R&D), Web Science Lab, IIIT-Bangalore): [email protected]

Web Chairs:
Bhoomika A P (PhD Scholar) WSL, IIIT Bangalore :[email protected]
Aparna M (M.S Scholar) WSL, IIIT Bangalore: [email protected]


Contact:
Rajeev Gupta (Principal scientist, Microsoft, India): [email protected]
Microsoft R&D India Pvt. Ltd., Hyderabad, India. Ph. +91-98186-62176.