A Tripartite Perspective on GraphRAG

• URL: http://arxiv.org/abs/2504.19667v1
• Date: Mon, 28 Apr 2025 10:43:35 GMT
• Title: A Tripartite Perspective on GraphRAG
• Authors: Michael Banf, Johannes Kuhn,
• Abstract summary: Large Language Models (LLMs) have shown remarkable capabilities across various domains, yet they struggle with knowledge-intensive tasks.<n>Key limitations include their tendency to hallucinate, lack of source traceability (provenance), and challenges in timely knowledge updates.<n>We propose a novel approach that combines LLMs with a tripartite knowledge graph representation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various domains, yet they struggle with knowledge-intensive tasks in areas that demand factual accuracy, e.g. industrial automation and healthcare. Key limitations include their tendency to hallucinate, lack of source traceability (provenance), and challenges in timely knowledge updates. Combining language models with knowledge graphs (GraphRAG) offers promising avenues for overcoming these deficits. However, a major challenge lies in creating such a knowledge graph in the first place. Here, we propose a novel approach that combines LLMs with a tripartite knowledge graph representation, which is constructed by connecting complex, domain-specific objects via a curated ontology of corresponding, domain-specific concepts to relevant sections within chunks of text through a concept-anchored pre-analysis of source documents starting from an initial lexical graph. As a consequence, our Tripartite-GraphRAG approach implements: i) a concept-specific, information-preserving pre-compression of textual chunks; ii) allows for the formation of a concept-specific relevance estimation of embedding similarities grounded in statistics; and iii) avoids common challenges w.r.t. continuous extendability, such as the need for entity resolution and deduplication. By applying a transformation to the knowledge graph, we formulate LLM prompt creation as an unsupervised node classification problem, drawing on ideas from Markov Random Fields. We evaluate our approach on a healthcare use case, involving multi-faceted analyses of patient anamneses given a set of medical concepts as well as clinical literature. Experiments indicate that it can optimize information density, coverage, and arrangement of LLM prompts while reducing their lengths, which may lead to reduced costs and more consistent and reliable LLM outputs.

Related papers

• Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs [4.701165676405066]
  It is critical not only to retrieve relevant information but also to provide causal reasoning and explainability.<n>This paper proposes a novel pipeline that filters large knowledge graphs to emphasize cause-effect edges.<n> Experiments on medical question-answering tasks show consistent gains, with up to a 10% absolute improvement.
  arXiv  Detail & Related papers  (2025-01-24T19:31:06Z)
• Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks [25.720233631885726]
  integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) has emerged as a promising technological paradigm. We leverage graph description texts with rich semantic context to fundamentally enhance Data quality. This work serves as a foundational reference for researchers and practitioners looking to advance graph learning methodologies.
  arXiv  Detail & Related papers  (2024-12-17T01:41:17Z)
• Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective [5.769786334333616]
  Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) based applications including automated text generation, question answering, and others. They face a significant challenge: hallucinations, where models produce plausible-sounding but factually incorrect responses. This paper discusses these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations.
  arXiv  Detail & Related papers  (2024-11-21T16:09:05Z)
• Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval [14.58181631462891]
  Large language models (LLMs) have demonstrated remarkable capabilities across various domains. Their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare. We propose Self-Refinement-Enhanced Knowledge Graph Retrieval (Re-KGR) to augment the factuality of LLMs' responses.
  arXiv  Detail & Related papers  (2024-05-10T15:40:50Z)
• Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models [23.438388321411693]
  Causal graph recovery is traditionally done using statistical estimation-based methods or based on individual's knowledge about variables of interests. We propose a novel method that leverages large language models (LLMs) to deduce causal relationships in general causal graph recovery tasks.
  arXiv  Detail & Related papers  (2024-02-23T13:02:10Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [57.052160123387104]
  We present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-10-27T14:00:04Z)
• Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction [104.29108668347727]
  This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models. The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies. We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.
  arXiv  Detail & Related papers  (2023-07-03T16:01:45Z)
• Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare [0.757843972001219]
  This paper presents an end-to-end representation learning framework to extract entity-centric KGs from structured and unstructured data. We introduce a star-shaped classifier to represent the multiple facets of a person and use it to guide KG creation. We highlight that this approach has several potential applications across domains and is open-sourced.
  arXiv  Detail & Related papers  (2023-05-09T17:39:45Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.