Enhancing Knowledge Graph Construction Using Large Language Models

• URL: http://arxiv.org/abs/2305.04676v1
• Date: Mon, 8 May 2023 12:53:06 GMT
• Title: Enhancing Knowledge Graph Construction Using Large Language Models
• Authors: Milena Trajanoska (1), Riste Stojanov (2), Dimitar Trajanov (3) ((1) Faculty of Computer Science and Engineering - Ss. Cyril and Methodius University - Skopje Macedonia)
• Abstract summary: This paper analyzes how the current advances in foundational LLM, like ChatGPT, can be compared with the specialized pretrained models, like REBEL, for joint entity and relation extraction. We created pipelines for the automatic creation of Knowledge Graphs from raw texts, and our findings indicate that using advanced LLM models can improve the accuracy of the process of creating these graphs from unstructured text.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models with semantic technologies for reasoning and inference is still a challenging task. This paper analyzes how the current advances in foundational LLM, like ChatGPT, can be compared with the specialized pretrained models, like REBEL, for joint entity and relation extraction. To evaluate this approach, we conducted several experiments using sustainability-related text as our use case. We created pipelines for the automatic creation of Knowledge Graphs from raw texts, and our findings indicate that using advanced LLM models can improve the accuracy of the process of creating these graphs from unstructured text. Furthermore, we explored the potential of automatic ontology creation using foundation LLM models, which resulted in even more relevant and accurate knowledge graphs.

Related papers

• Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation [7.3491970177535]
  This study proposes a scheme to process graph structure data by combining graph neural network (GNN) The results show that the graph-based RAG model proposed in this paper is superior to the traditional generation model in terms of quality, knowledge consistency, and reasoning ability.
  arXiv  Detail & Related papers  (2024-11-06T00:23:55Z)
• Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document [7.0421339410165045]
  This study introduces a novel approach to sentence-level relation extraction (RE) It integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. Our experiments, conducted on the CrossRE dataset, demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-10-30T20:48:34Z)
• 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)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models [57.12888828853409]
  RAVEN is a model that combines retrieval-augmented masked language modeling and prefix language modeling. Fusion-in-Context Learning enables the model to leverage more in-context examples without requiring additional training. Our work underscores the potential of retrieval-augmented encoder-decoder language models for in-context learning.
  arXiv  Detail & Related papers  (2023-08-15T17:59:18Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text [3.114960935006655]
  This paper aims to improve the state of the art of automatic construction and completion of knowledge graphs from text. In this context, one emerging paradigm is in-context learning where a language model is used as it is with a prompt.
  arXiv  Detail & Related papers  (2023-05-15T17:10:19Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• Interpreting Language Models Through Knowledge Graph Extraction [42.97929497661778]
  We compare BERT-based language models through snapshots of acquired knowledge at sequential stages of the training process. We present a methodology to unveil a knowledge acquisition timeline by generating knowledge graph extracts from cloze "fill-in-the-blank" statements. We extend this analysis to a comparison of pretrained variations of BERT models (DistilBERT, BERT-base, RoBERTa)
  arXiv  Detail & Related papers  (2021-11-16T15:18:01Z)

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.