Construction of Knowledge Graph based on Language Model

• URL: http://arxiv.org/abs/2604.19137v1
• Date: Tue, 21 Apr 2026 06:38:07 GMT
• Title: Construction of Knowledge Graph based on Language Model
• Abstract summary: Knowledge Graph (KG) can effectively integrate valuable information from massive data.<n>Traditional KG construction methods rely on manual annotation, which often consumes a lot of time and manpower.<n>With the rapid development of Pre-trained Language Models (PLM), PLM has shown great potential in the field of KG construction.
• Score: 9.991709321376296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Graph (KG) can effectively integrate valuable information from massive data, and thus has been rapidly developed and widely used in many fields. Traditional KG construction methods rely on manual annotation, which often consumes a lot of time and manpower. And KG construction schemes based on deep learning tend to have weak generalization capabilities. With the rapid development of Pre-trained Language Models (PLM), PLM has shown great potential in the field of KG construction. This paper provides a comprehensive review of recent research advances in the field of construction of KGs using PLM. In this paper, we explain how PLM can utilize its language understanding and generation capabilities to automatically extract key information for KGs, such as entities and relations, from textual data. In addition, We also propose a new Hyper-Relarional Knowledge Graph construction framework based on lightweight Large Language Model (LLM) named LLHKG and compares it with previous methods. Under our framework, the KG construction capability of lightweight LLM is comparable to GPT3.5.

Related papers

• KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering [64.63418702318029]
  KG2Code is a novel approach that transforms knowledge graphs into a code-based representation.<n> KG2Code-QA is introduced as a KGQA framework that formulates KGQA as a code generation task.
  arXiv  Detail & Related papers  (2026-06-26T11:27:55Z)
• Beyond Textual Context: Structural Graph Encoding with Adaptive Space Alignment to alleviate the hallucination of LLMs [15.260879306368674]
  The SSKG-LLM is an innovative model architecture that efficiently integrate both the Structural information of KGs into the reasoning processes of Large Language Models.<n>We conduct extensive experiments and provide a detailed analysis to explore how incorporating the structural information of KGs can enhance the factual reasoning abilities of LLMs.
  arXiv  Detail & Related papers  (2025-09-26T12:14:01Z)
• 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.<n>Specifically, we introduce an improved Graph Transformer (iGT) that effectively encodes subgraphs with both local and global structural information.<n>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)
• Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models [19.28217321004791]
  Large Language Models (LLMs) offer a promising way to improve the quality and relevance of Knowledge Graphs for recommendation tasks.<n>We propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task.<n>The framework includes: (1) an LLM-based subgraph augmenter for enriching KGs with high-quality information, (2) a confidence-aware message propagation mechanism to filter noisy triplets, and (3) a dual-view contrastive learning method to integrate user-item interactions and KG data.
  arXiv  Detail & Related papers  (2025-02-06T02:06:48Z)
• Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema [60.42231674887294]
  We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base.<n>We ground generation of KG with the authored ontology based on extracted relations to ensure consistency and interpretability.<n>Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs.
  arXiv  Detail & Related papers  (2024-12-30T13:36:05Z)
• 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.<n>We first convert structured knowledge graph data into natural language and then use these natural language prompts to fine-tune LLMs.<n>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)
• Unifying Large Language Models and Knowledge Graphs: A Roadmap [61.824618473293725]
  Large language models (LLMs) are making new waves in the field of natural language processing and artificial intelligence. Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge.
  arXiv  Detail & Related papers  (2023-06-14T07:15:26Z)
• BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models [65.51390418485207]
  We propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition, the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge. We deploy the approach to harvest KGs of over 400 new relations from different LMs.
  arXiv  Detail & Related papers  (2022-06-28T19:46:29Z)
• Language Models are Open Knowledge Graphs [75.48081086368606]
  Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora.
  arXiv  Detail & Related papers  (2020-10-22T18:01:56Z)

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.