mlsanityData to 7 Oct 2026

GraphAide: Advanced Graph-Assisted Query and Reasoning System

arXiv 2411.08041 ↗First posted 29 Oct 2024cs.DB cs.AIcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 12 of its 19 references that are in the map. First posted on arXiv 29 Oct 2024.

Builds on

12 of 19 references.

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Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  5. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  6. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  7. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  8. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 168
  9. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  10. 2023Enhancing Knowledge Graph Construction Using Large Language Modelscited by 44
  11. 2020Semantic Property Graph for Scalable Knowledge Graph Analyticscited by 2

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  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  2. 2025GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
  3. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  4. 2026GraphSeek: Next-Generation Graph Analytics with LLMs
  5. 2024Towards Evaluating Large Language Models for Graph Query Generation
  6. 2025Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
  7. 2024Graph Retrieval-Augmented Generation: A Survey
  8. 2025Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching