Top Ten Challenges Towards Agentic Neural Graph Databases
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use
While Retrieval-Augmented Generation (RAG) has proven effective for generating accurate, context-based responses based on existing knowledge bases, it presents several challenges including retrieval quality dependencies,…
Information RetrievalMaterial Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining
Materials science workflows rely on structured and unstructured data from the vast body of available scientific literature. However, most of the experimental details remain buried in text, tables, graphs and figures. Thu…
Information ExtractionHippocampus: An Efficient and Scalable Memory Module for Agentic AI
Agentic AI require persistent memory to store user-specific histories beyond the limited context window of LLMs. Existing memory systems use dense vector databases or knowledge-graph traversal (or hybrid), incurring high…
ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL
Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large and heterogeneous schemas, incomplete m…
Agentic Publications: An LLM-Driven Framework for Interactive Scientific Publishing, Supplementing Traditional Papers with AI-Powered Knowledge Systems
The exponential growth of scientific literature presents significant challenges for researchers navigating the complex knowledge landscape. We propose "Agentic Publications", a novel LLM-driven framework complementing tr…
Knowledge GraphsRetrieval-augmented Generation