KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using
Large Language Models
- URL: http://arxiv.org/abs/2310.11220v1
- Date: Tue, 17 Oct 2023 12:51:35 GMT
- Title: KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using
Large Language Models
- Authors: Jiho Kim, Yeonsu Kwon, Yohan Jo, Edward Choi
- Abstract summary: We propose KG-GPT, a framework leveraging large language models for tasks employing knowledge graphs.
KG-GPT comprises three steps: Sentence, Graph Retrieval, and Inference, each aimed at partitioning sentences, retrieving relevant graph components, and deriving logical conclusions.
We evaluate KG-GPT using KG-based fact verification and KGQA benchmarks, with the model showing competitive and robust performance, even outperforming several fully-supervised models.
- Score: 18.20425100517317
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While large language models (LLMs) have made considerable advancements in
understanding and generating unstructured text, their application in structured
data remains underexplored. Particularly, using LLMs for complex reasoning
tasks on knowledge graphs (KGs) remains largely untouched. To address this, we
propose KG-GPT, a multi-purpose framework leveraging LLMs for tasks employing
KGs. KG-GPT comprises three steps: Sentence Segmentation, Graph Retrieval, and
Inference, each aimed at partitioning sentences, retrieving relevant graph
components, and deriving logical conclusions, respectively. We evaluate KG-GPT
using KG-based fact verification and KGQA benchmarks, with the model showing
competitive and robust performance, even outperforming several fully-supervised
models. Our work, therefore, marks a significant step in unifying structured
and unstructured data processing within the realm of LLMs.
Related papers
- Grounding LLM Reasoning with Knowledge Graphs [4.279373869671241]
We propose integrating reasoning strategies with Knowledge Graphs to anchor every step or "thought" of the reasoning chains in KG data.
We evaluate both agentic and automated search methods across several reasoning strategies, including Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT)
Our experiments demonstrate that this approach consistently outperforms baseline models.
arXiv Detail & Related papers (2025-02-18T19:20:46Z) - GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion [52.026016846945424]
We propose a new method called GLTW, which encodes the structural information of KGs and merges it with Large Language Models.
Specifically, we introduce an improved Graph Transformer (iGT) that effectively encodes subgraphs with both local and global structural information.
Also, we develop a subgraph-based multi-classification training objective, using all entities within KG as classification objects, to boost learning efficiency.
arXiv Detail & Related papers (2025-02-17T06:02:59Z) - KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models [55.39134076436266]
KG-CF is a framework tailored for ranking-based knowledge graph completion tasks.
KG-CF leverages LLMs' reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets.
arXiv Detail & Related papers (2025-01-06T01:52:15Z) - Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains [66.55612528039894]
Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA)
We present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs.
Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance.
arXiv Detail & Related papers (2024-10-24T04:01:40Z) - Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications.
Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora.
We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs.
We also design a novel graph-based retrieval framework for RAG.
arXiv Detail & Related papers (2024-10-22T00:47:54Z) - Context Graph [8.02985792541121]
We present a context graph reasoning textbfCGR$3$ paradigm that leverages large language models (LLMs) to retrieve candidate entities and related contexts.
Our experimental results demonstrate that CGR$3$ significantly improves performance on KG completion (KGC) and KG question answering (KGQA) tasks.
arXiv Detail & Related papers (2024-06-17T02:59:19Z) - Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs)
GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
arXiv Detail & Related papers (2024-04-23T04:47:22Z) - Knowledge Graph Large Language Model (KG-LLM) for Link Prediction [43.55117421485917]
We introduce the Knowledge Graph Large Language Model (KG-LLM), a novel framework that leverages large language models (LLMs) for knowledge graph tasks.
We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs.
To show the efficacy of the KG-LLM Framework, we fine-tune three leading LLMs within this framework, including Flan-T5, LLaMa2 and Gemma.
arXiv Detail & Related papers (2024-03-12T04:47:29Z) - Multi-perspective Improvement of Knowledge Graph Completion with Large
Language Models [95.31941227776711]
We propose MPIKGC to compensate for the deficiency of contextualized knowledge and improve KGC by querying large language models (LLMs)
We conducted extensive evaluation of our framework based on four description-based KGC models and four datasets, for both link prediction and triplet classification tasks.
arXiv Detail & Related papers (2024-03-04T12:16:15Z)
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.