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Top Ten Challenges Towards Agentic Neural Graph Databases

Jiaxin Bai, Zihao Wang, Yukun Zhou, Hang Yin, Weizhi Fei, Qi Hu, Zheye Deng, Jiayang Cheng, Tianshi Zheng, Hong Ting Tsang, Yisen Gao, Zhongwei Xie, Yufei Li, Lixin Fan, Binhang Yuan, Wei Wang, Lei Chen, Xiaofang Zhou, Yangqiu Song

TL;DR

Agentic NGDBs extend Neural Graph Databases by coupling autonomous query construction, neural query execution, and continuous learning to enable self-improving data management. The paper outlines ten intertwined challenges across interface, learning, and system dimensions, including semantic unit representation, abductive reasoning, generalization across query families, privacy, scalability, distributed deployment, compatibility with traditional GDBs, vector grounding, and integration with LLMs. It discusses methods such as neuro-symbolic reasoning, query embeddings, and NGDB-RAG for LLM collaboration, and highlights the need for robust privacy defenses and elastic, cloud-native distributed architectures. If solved, Agentic NGDBs promise autonomous data management with adaptive reasoning, personalized recommendations, and real-time complex event processing in large-scale, interconnected data systems.

Abstract

Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined queries and lack autonomy and adaptability. This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning. We identify ten key challenges in realizing Agentic NGDBs: semantic unit representation, abductive reasoning, scalable query execution, and integration with foundation models like large language models (LLMs). By addressing these challenges, Agentic NGDBs can enable intelligent, self-improving systems for modern data-driven applications, paving the way for adaptable and autonomous data management solutions.

Top Ten Challenges Towards Agentic Neural Graph Databases

TL;DR

Agentic NGDBs extend Neural Graph Databases by coupling autonomous query construction, neural query execution, and continuous learning to enable self-improving data management. The paper outlines ten intertwined challenges across interface, learning, and system dimensions, including semantic unit representation, abductive reasoning, generalization across query families, privacy, scalability, distributed deployment, compatibility with traditional GDBs, vector grounding, and integration with LLMs. It discusses methods such as neuro-symbolic reasoning, query embeddings, and NGDB-RAG for LLM collaboration, and highlights the need for robust privacy defenses and elastic, cloud-native distributed architectures. If solved, Agentic NGDBs promise autonomous data management with adaptive reasoning, personalized recommendations, and real-time complex event processing in large-scale, interconnected data systems.

Abstract

Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined queries and lack autonomy and adaptability. This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning. We identify ten key challenges in realizing Agentic NGDBs: semantic unit representation, abductive reasoning, scalable query execution, and integration with foundation models like large language models (LLMs). By addressing these challenges, Agentic NGDBs can enable intelligent, self-improving systems for modern data-driven applications, paving the way for adaptable and autonomous data management solutions.
Paper Structure (31 sections, 1 figure)

This paper contains 31 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Interface
  3. Learning and Inference
  4. System
  5. Challenge 1: Semantic Units
  6. Challenge 2: Abductive Reasoning with NGDB
  7. Challenge 3: Generalization across Query Families
  8. Different Query Families and Their Neural Modules
  9. Tree-formed Queries and Compositional Generalizability.
  10. EFO-1 Queries and Query Graph.
  11. More Advanced Query Types.
  12. Minimal Assumption for Broad Generalization
  13. Learning Aspects of Generalization
  14. Challenge 4: Privacy and Security
  15. Database Privacy
  16. ...and 16 more sections

Figures (1)

  • Figure 1: The top ten challenges in achieving Agentic NGDB. Its three perspectives include interface, learning, and system.